Microsoft DP-300 Administering Azure SQL Solutions Exam Dumps and Practice Test Questions Set 4 Q 46-60
Visit here for our full Microsoft DP-300 exam dumps and practice test questions.
Question 46:
You are administering an Azure SQL Database that experiences unpredictable workload patterns throughout the day. You need to ensure the database can automatically scale compute resources based on demand while minimizing costs during low-usage periods. Which purchasing model should you implement?
A) DTU-based purchasing model with Standard tier
B) vCore-based purchasing model with General Purpose tier
C) Serverless compute tier in the vCore-based model
D) Hyperscale service tier with read replicas
Answer: C
Explanation:
Azure SQL Database offers multiple purchasing models and service tiers designed to accommodate different workload patterns and cost optimization requirements. When dealing with unpredictable workload patterns that require automatic scaling based on demand while minimizing costs during periods of inactivity, the serverless compute tier in the vCore-based purchasing model is the optimal solution, making option C the correct answer.
The serverless compute tier is specifically designed for single databases with intermittent and unpredictable usage patterns. This tier provides automatic compute scaling based on workload demand, allowing the database to scale up during periods of high activity and scale down during quiet periods without manual intervention. The most significant cost-saving feature is automatic pause and resume functionality, where the database automatically pauses after a configurable period of inactivity, during which time customers are only charged for storage rather than compute resources. When activity resumes, the database automatically wakes up within seconds. The serverless tier allows configuration of minimum and maximum vCore limits to control scaling boundaries and costs, charges are based on actual compute usage per second rather than fixed hourly rates, and billing occurs only for the compute resources actually consumed. This makes serverless ideal for development and test environments, applications with sporadic usage patterns, new applications with uncertain capacity requirements, and cost-sensitive workloads where performance can tolerate brief delays during resume operations. Configuration parameters include auto-pause delay which determines inactivity duration before pausing, minimum vCores for baseline performance, and maximum vCores for peak capacity. The serverless tier combines cost efficiency with automatic performance management, eliminating the need for manual scaling or over-provisioning resources.
Option A, the DTU-based purchasing model with Standard tier, provides fixed compute resources based on Database Transaction Units that combine CPU, memory, and IO into a single metric. While the Standard tier is cost-effective for moderate workloads, it does not automatically scale based on demand or pause during inactivity. Resources remain allocated and incur charges continuously regardless of actual usage levels. Manual intervention is required to scale between DTU tiers, making this option unsuitable for unpredictable workloads requiring automatic cost optimization.
Option B, the vCore-based purchasing model with General Purpose tier, offers more granular control over compute and storage resources compared to DTU-based models and provides better price-performance for many workloads. However, the standard provisioned compute in General Purpose tier allocates fixed resources that remain active continuously, charging for the full allocated capacity regardless of actual utilization. While you can manually scale vCores up or down, this requires administrative intervention and does not provide automatic scaling or pause capabilities for cost optimization during idle periods.
Option D, the Hyperscale service tier with read replicas, is designed for very large databases requiring massive scale-out capabilities, supporting databases up to 100 TB with fast backup and restore operations. Hyperscale provides excellent performance through multiple read replicas and innovative storage architecture, but it is a premium tier with higher costs that remain constant regardless of workload patterns. Hyperscale does not automatically pause during inactivity and is intended for large-scale production workloads rather than cost optimization for intermittent usage patterns described in the question.
Question 47:
You need to implement high availability for an Azure SQL Database instance that requires automatic failover to a secondary region in case of regional outage. Which feature should you configure?
A) Active geo-replication
B) Zone-redundant configuration
C) Auto-failover groups
D) Point-in-time restore
Answer: C
Explanation:
High availability and disaster recovery are critical considerations when administering Azure SQL Database, particularly for production workloads that require minimal downtime during regional failures. While Azure provides multiple features for data protection and availability, auto-failover groups offer the most comprehensive solution for automatic regional failover with minimal application impact, making option C the correct answer.
Auto-failover groups provide automatic failover orchestration for a group of databases from a primary server to a secondary server in a different Azure region. This feature builds upon active geo-replication but adds critical automation and management capabilities that simplify disaster recovery implementation. Key benefits include automatic failover without manual intervention when the primary region becomes unavailable, read-write and read-only listener endpoints that automatically redirect connections to the current primary server, support for multiple databases in a single failover group enabling coordinated failover, configurable grace periods before automatic failover is triggered, and transparent redirection for applications using the listener endpoints. The listener endpoints are particularly valuable because applications can use a consistent connection string pointing to the listener rather than specific server names. When failover occurs, the listener automatically updates to point to the new primary server, eliminating the need for application configuration changes or DNS updates. Auto-failover groups support customizable failover policies including manual failover for planned maintenance, automatic failover for unplanned outages, and configurable data loss tolerance through Read-Write failover policy settings. Implementation involves creating a failover group between primary and secondary servers, adding databases to the group, configuring failover policies, and updating applications to use listener endpoints. This provides robust disaster recovery with minimal administrative overhead and application complexity.
Option A, active geo-replication, creates readable secondary database replicas in the same or different Azure regions, providing the foundation for disaster recovery scenarios. While active geo-replication enables manual failover to secondary replicas and supports up to four readable secondaries, it does not provide automatic failover capabilities. When the primary region fails, administrators must manually initiate failover to a secondary replica, requiring monitoring, detection, and manual intervention. Additionally, applications must be updated to point to the new primary server after failover, creating additional complexity and potential downtime during the failover process.
Option B, zone-redundant configuration, provides high availability within a single Azure region by distributing database replicas across multiple availability zones within that region. This configuration protects against datacenter-level failures within a region and provides automatic failover between zones without application intervention. However, zone redundancy does not protect against regional outages or provide secondary regions for disaster recovery. If an entire region becomes unavailable, zone-redundant databases in that region would also be unavailable, making this insufficient for the cross-region automatic failover requirement specified in the question.
Option D, point-in-time restore, is a backup and recovery feature that allows restoring databases to any point within the retention period, typically 7 to 35 days depending on configuration. PITR protects against data corruption, accidental deletions, or application errors by enabling recovery to a specific timestamp. However, point-in-time restore creates a new database from backups and requires manual initiation, taking minutes to hours depending on database size. This feature does not provide high availability, automatic failover, or protection against regional outages, making it inappropriate for the requirements described in the question.
Question 48:
You are monitoring an Azure SQL Database and notice high DTU consumption. You need to identify which queries are consuming the most resources. Which Azure feature should you use?
A) Azure Activity Log
B) Query Performance Insight
C) Azure Service Health
D) Azure Resource Graph
Answer: B
Explanation:
Performance monitoring and optimization are essential responsibilities when administering Azure SQL Database, and identifying resource-intensive queries is often the first step in resolving performance issues. Azure provides several monitoring tools, but Query Performance Insight is specifically designed to identify and analyze queries consuming the most database resources, making option B the correct answer.
Query Performance Insight is a built-in intelligent performance monitoring feature integrated directly into Azure SQL Database that provides visualization and analysis of query performance and resource consumption. This tool automatically collects and analyzes query execution statistics, presenting them in an intuitive dashboard that displays the top resource-consuming queries by CPU, duration, execution count, or IO consumption. Key capabilities include identifying queries that contribute most to DTU or vCore utilization, viewing query execution statistics over configurable time periods, drilling down into individual query details including execution plans and statistics, comparing query performance across different time ranges, identifying queries with performance degradation trends, and accessing query text and execution context. Query Performance Insight uses the built-in Query Store feature which continuously captures query execution information without requiring manual configuration or application changes. The dashboard categorizes queries by resource consumption metrics, allowing administrators to quickly identify problematic queries that should be optimized through indexing, query rewriting, or parameter adjustments. Additional features include recommendations for missing indexes that could improve query performance, alerts for queries showing performance regression, and integration with other Azure monitoring tools. This targeted analysis enables rapid identification of performance bottlenecks and provides the information necessary for effective query optimization efforts.
Option A, Azure Activity Log, records subscription-level events and operations performed on Azure resources, including who performed operations, when they occurred, and the operation status. Activity Log captures management plane activities such as resource creation, deletion, configuration changes, role assignments, and service health events. While valuable for auditing administrative actions and tracking resource changes, Activity Log does not provide query-level performance metrics or information about which database queries are consuming resources. This tool operates at the resource management level rather than the database query execution level.
Option C, Azure Service Health, provides personalized alerts and guidance when Azure service issues affect your resources, including planned maintenance notifications, service outage alerts, and health advisories. Service Health helps administrators understand when Azure platform issues might be affecting database performance or availability. However, this service focuses on Azure infrastructure health rather than database query performance, and cannot identify specific queries consuming high DTU within a database. Service Health is valuable for understanding external factors affecting databases but does not provide query-level performance analysis.
Option D, Azure Resource Graph, is a powerful query service that enables exploration and analysis of Azure resources at scale across subscriptions. Resource Graph allows complex queries using Kusto Query Language (KQL) to inventory resources, track changes, and analyze configurations across large Azure environments. While extremely useful for resource management and compliance scenarios, Resource Graph operates at the resource metadata level and does not access database internals or query execution statistics. This tool cannot identify which SQL queries are consuming DTU within a specific database, making it inappropriate for the scenario described in the question.
Question 49:
You need to implement data masking for sensitive columns in an Azure SQL Database to prevent unauthorized users from viewing complete credit card numbers. Which Azure SQL Database security feature should you use?
A) Transparent Data Encryption (TDE)
B) Dynamic Data Masking
C) Always Encrypted
D) Row-Level Security
Answer: B
Explanation:
Protecting sensitive data from unauthorized access is a fundamental security requirement when administering Azure SQL Database, and different security features address different aspects of data protection. When the requirement is to prevent unauthorized users from viewing complete sensitive data such as credit card numbers while still allowing authorized users to access full data, Dynamic Data Masking is the appropriate feature, making option B the correct answer.
Dynamic Data Masking (DDM) is a policy-based security feature that limits sensitive data exposure by masking it in query results for non-privileged users without modifying the actual data stored in the database. This feature applies masking rules to designated columns, automatically obscuring sensitive data when queried by users who lack appropriate permissions. Masking occurs at the presentation layer in real-time as data is returned from queries, meaning the underlying data remains unchanged and fully accessible to authorized users with UNMASK permission. Azure SQL Database provides several built-in masking functions including default masking which uses full masking for strings (XXXX), zeros for numeric types, and 1900-01-01 for dates; email masking which exposes only the first letter and domain suffix; custom string masking allowing specification of exposed prefix and suffix lengths; and random number masking for numeric columns. For credit card numbers specifically, administrators would typically use custom string masking to show only the last four digits, such as configuring a mask to display «XXXX-XXXX-XXXX-1234» instead of the full number. Implementation involves defining masking rules on specific columns and granting UNMASK permission to users or roles requiring access to complete data. Dynamic Data Masking is particularly effective for limiting sensitive data exposure in production databases accessed by developers, report viewers, or support personnel who need database access but should not view complete sensitive values.
Option A, Transparent Data Encryption (TDE), encrypts data at rest by performing real-time encryption and decryption of database files, backups, and transaction logs at the storage level. TDE protects against threats of malicious activity by encrypting the physical database files, preventing unauthorized access if storage media or backup files are compromised. However, TDE operates transparently to applications and does not distinguish between users; all authenticated users querying the database receive unencrypted data in query results. TDE provides encryption at rest but does not mask data from unauthorized database users, making it unsuitable for the requirement to prevent specific users from viewing complete credit card numbers.
Option C, Always Encrypted, is a client-side encryption technology that encrypts sensitive data inside client applications, ensuring that encryption keys and plaintext data never appear within the database engine. With Always Encrypted, data remains encrypted at rest, in transit, and during query processing, with decryption occurring only on the client side for applications possessing the appropriate encryption keys. While this provides the strongest protection for sensitive data, it significantly limits database functionality because encrypted columns cannot be used in WHERE clauses, joins, indexes, or other SQL operations without special enclave-enabled configurations. Always Encrypted is more complex to implement than Dynamic Data Masking and is typically reserved for highly sensitive scenarios requiring end-to-end encryption rather than simple masking for specific users.
Option D, Row-Level Security (RLS), implements access control at the row level, allowing administrators to define security policies that filter which rows users can access based on user characteristics or execution context. RLS is ideal for multi-tenant scenarios or situations where different users should access different subsets of data within the same table, such as salespeople viewing only their own customers or managers viewing their department’s records. However, RLS controls which rows are visible, not which columns or how data within those rows is displayed. RLS would not mask credit card numbers; it would either allow or deny access to entire rows containing those numbers, making it inappropriate for column-level sensitive data masking requirements.
Question 50:
You are configuring backups for an Azure SQL Database. The business requires the ability to restore the database to any point within the last 14 days. Which backup feature provides this capability?
A) Long-term retention (LTR)
B) Point-in-time restore (PITR)
C) Geo-redundant backups
D) Manual database export
Answer: B
Explanation:
Backup and recovery capabilities are fundamental components of database administration, and Azure SQL Database provides multiple backup features designed for different recovery scenarios and retention requirements. When the requirement is to restore a database to any specific point in time within a defined retention period, point-in-time restore is the appropriate feature, making option B the correct answer.
Point-in-time restore (PITR) is an automatic backup feature in Azure SQL Database that enables recovery to any point within the retention period by leveraging a combination of full, differential, and transaction log backups. Azure SQL Database automatically performs full backups weekly, differential backups every 12-24 hours, and transaction log backups every 5-10 minutes, creating a continuous backup chain that supports granular recovery. The retention period for point-in-time restore is configurable from 1 to 35 days, with the default being 7 days for most service tiers. For the business requirement of 14-day restore capability, administrators would configure the backup retention period to 14 days or greater. PITR restoration creates a new database on the same or different server within the same region, allowing recovery without overwriting the existing database. This feature protects against various scenarios including accidental data deletion or corruption, application errors that modify data incorrectly, testing and development scenarios requiring production data copies, and compliance requirements for data recovery capabilities. The restore operation preserves the database service tier, compute size, and backup storage redundancy of the source database. Restore time depends on database size and transaction log activity but typically completes within minutes to hours. PITR is included automatically with all Azure SQL Database service tiers without additional configuration, though retention period adjustments may affect backup storage costs.
Option A, long-term retention (LTR), extends backup retention far beyond the standard 35-day maximum for point-in-time restore, supporting retention policies up to 10 years for compliance and regulatory requirements. LTR policies allow weekly, monthly, or yearly full backup retention with independent retention periods for each frequency. While LTR provides extended retention, it only supports full database restores from specific backup points rather than point-in-time recovery to any moment within the retention window. For the requirement to restore to any point within 14 days, standard PITR is more appropriate than LTR, which is designed for long-term compliance rather than recent operational recovery scenarios.
Option C, geo-redundant backups, refers to the storage redundancy option for backup files rather than a distinct backup feature. Azure SQL Database backups can be configured with locally-redundant storage (LRS), zone-redundant storage (ZRS), or geo-redundant storage (GRS), which replicates backups to a paired Azure region. Geo-redundant backup storage enables geo-restore, allowing database restoration in a different region during regional outages. However, geo-redundancy is a storage option that works in conjunction with PITR or LTR rather than a separate backup capability. The question asks about the feature enabling point-in-time recovery, not the backup storage redundancy option.
Option D, manual database export, creates a BACPAC file containing database schema and data that can be downloaded or stored in Azure Blob Storage. While exports provide portable database copies useful for migration, archival, or offline storage, they represent snapshot backups at specific points in time and must be initiated manually or through scheduled automation. Manual exports do not provide the continuous point-in-time recovery capability needed to restore to any moment within a 14-day window. Additionally, importing a BACPAC file is significantly slower than restoring from automated backups, making this approach unsuitable for operational recovery scenarios requiring precise point-in-time restoration.
Question 51:
You need to implement a solution that automatically tunes and optimizes query performance in Azure SQL Database without manual intervention. Which feature should you enable?
A) Query Store
B) Automatic tuning
C) Database Advisor
D) Intelligent Insights
Answer: B
Explanation:
Performance optimization in database administration traditionally requires continuous monitoring, analysis, and manual implementation of tuning recommendations. Azure SQL Database includes intelligent automation features that can automatically identify and implement performance improvements, with automatic tuning being the most comprehensive solution for hands-off query optimization, making option B the correct answer.
Automatic tuning in Azure SQL Database uses artificial intelligence and machine learning to continuously monitor database performance, identify optimization opportunities, and automatically implement proven tuning actions without manual intervention. This feature builds upon the Query Store foundation and includes three primary automatic tuning options: CREATE INDEX which automatically creates missing indexes that could improve query performance, DROP INDEX which removes duplicate or unused indexes that consume resources without providing benefits, and FORCE LAST GOOD PLAN which detects queries whose execution plans have regressed and automatically reverts to the previous better-performing plan. The system validates all tuning actions before full implementation through a rigorous verification process that includes testing the proposed change, measuring performance impact, and automatically rolling back changes that do not provide improvements or cause performance degradation. Automatic tuning operates continuously and adapts to changing workload patterns, making it ideal for dynamic applications where manual tuning would require constant attention. Administrators can configure which automatic tuning options are enabled, review tuning actions through Azure portal or T-SQL views, and override automatic decisions if necessary. The verification and rollback mechanism ensures safe operation without risk of performance degradation, as any change that reduces performance is immediately reversed. Automatic tuning provides detailed logging and reporting of all tuning actions, performance improvements achieved, and rationale for decisions, enabling administrators to understand optimization actions even when operating autonomously.
Option A, Query Store, is the foundational feature that captures and retains query execution history, execution plans, and runtime statistics, providing the data necessary for performance analysis and optimization. Query Store acts as a «flight recorder» for database queries, storing information that survives server restarts and allows performance comparison across time periods. While Query Store is essential infrastructure that enables automatic tuning and other intelligent features, it does not automatically implement performance optimizations. Query Store provides the data and visibility for manual analysis or automated tuning but requires either administrator action or enabling automatic tuning to actually implement optimizations.
Option C, Database Advisor, analyzes database performance and provides recommendations for performance improvements including missing indexes, unused indexes, and parameterization opportunities. Database Advisor presents actionable recommendations through the Azure portal with estimated performance impact and implementation scripts. While extremely valuable for identifying optimization opportunities, Database Advisor requires administrators to review recommendations and manually decide whether to implement them. Unlike automatic tuning, Database Advisor does not automatically implement changes, making it a semi-automated solution requiring human review rather than fully automatic optimization.
Option D, Intelligent Insights, is an advanced monitoring feature that uses artificial intelligence to detect and diagnose performance issues in Azure SQL Database. This service analyzes telemetry data to identify problems such as query performance degradation, resource constraints, locking issues, or unusual workload patterns, providing diagnostic information and root cause analysis. Intelligent Insights generates diagnostics logs that can integrate with Azure Monitor, Log Analytics, or third-party monitoring solutions. While Intelligent Insights excels at detecting and explaining performance problems, it is a diagnostic and alerting tool rather than an optimization implementation mechanism. It identifies issues but does not automatically implement tuning actions to resolve them.
Question 52:
You are planning to migrate an on-premises SQL Server database to Azure SQL Database. The database contains a SQL Server Agent job that runs nightly maintenance tasks. What should you use to replace SQL Server Agent functionality in Azure SQL Database?
A) Azure Automation
B) Elastic jobs
C) Azure Data Factory
D) Azure Functions
Answer: B
Explanation:
Migrating from on-premises SQL Server to Azure SQL Database requires understanding feature differences and identifying appropriate alternatives for capabilities not directly available in the platform-as-a-service environment. SQL Server Agent is a Windows service that executes scheduled jobs, but it is not available in Azure SQL Database because customers do not have access to the underlying operating system. For database maintenance tasks and scheduled T-SQL execution in Azure SQL Database, elastic jobs provide the closest functional equivalent, making option B the correct answer.
Elastic jobs is a feature designed specifically for Azure SQL Database that enables creation and management of scheduled T-SQL jobs across one or multiple databases. This service provides capabilities similar to SQL Server Agent specifically tailored for cloud database scenarios, including scheduling jobs using cron expressions for flexible timing, executing T-SQL scripts against target databases, managing credentials for job execution, targeting individual databases or groups of databases, retrying failed executions automatically, and viewing job execution history and logs. Elastic jobs are particularly powerful for scenarios requiring execution across multiple databases such as schema deployments, data collection, or maintenance operations across database fleets. The service uses an elastic job agent which is a dedicated Azure SQL Database that stores job definitions, schedules, execution history, and coordinates job execution. Implementation involves creating an elastic job database and agent, defining target groups specifying which databases jobs will execute against, creating credentials for database access, defining job steps containing T-SQL scripts, and configuring schedules. Common use cases include index maintenance similar to traditional SQL Agent maintenance plans, running statistics updates, executing custom data archival or cleanup scripts, deploying schema changes across multiple databases, and collecting performance metrics or audit data. For organizations migrating SQL Agent jobs to Azure SQL Database, elastic jobs provide enterprise-grade job scheduling without requiring alternative Azure services outside the database platform.
Option A, Azure Automation, is a cloud-based automation service that provides process automation, configuration management, and update management capabilities. Azure Automation uses PowerShell or Python runbooks to automate repetitive tasks across Azure resources and can certainly execute database operations through PowerShell modules or REST APIs. While Azure Automation can invoke database operations, it requires additional complexity including authentication setup, error handling implementation, and custom scripting. For database-centric maintenance tasks traditionally handled by SQL Agent, Azure Automation introduces unnecessary complexity compared to elastic jobs which are purpose-built for database job scheduling.
Option C, Azure Data Factory, is a cloud-based data integration service designed for creating data-driven workflows that orchestrate and automate data movement and transformation. Data Factory excels at ETL and ELT scenarios involving data pipelines that move and transform data between various sources and destinations. While Data Factory can execute stored procedures and SQL scripts as part of data pipelines, it is architected for data integration workflows rather than database maintenance jobs. Using Data Factory for simple maintenance tasks like index rebuilds or statistics updates would be over-engineering the solution with a tool designed for more complex data orchestration scenarios.
Option D, Azure Functions, is a serverless compute service that enables running event-driven code without managing infrastructure. Functions can certainly be triggered on schedules using timer triggers and can execute database operations through appropriate client libraries or ORMs. While Azure Functions provides flexibility and serverless benefits, it requires application code development in supported languages rather than native T-SQL execution. For database administrators accustomed to SQL Agent jobs containing T-SQL scripts, Azure Functions introduces a different programming model and additional complexity. Elastic jobs maintain the familiar T-SQL scripting approach while providing the necessary scheduling and execution capabilities.
Question 53:
You need to configure an Azure SQL Database to ensure that data is encrypted during transmission between the database and client applications. Which security protocol should you enforce?
A) IPSec
B) Transport Layer Security (TLS)
C) SSH
D) PPTP
Answer: B
Explanation:
Protecting data during transmission is a critical security requirement for database systems, as network traffic can potentially be intercepted and analyzed by unauthorized parties. Azure SQL Database uses industry-standard protocols for securing data in transit, with Transport Layer Security being the primary encryption protocol for database connections, making option B the correct answer.
Transport Layer Security (TLS) is a cryptographic protocol that provides secure communication over networks by encrypting data transmitted between clients and servers. Azure SQL Database enforces TLS encryption for all connections by default, ensuring that data transmitted between client applications and the database is protected from eavesdropping and tampering. TLS encryption is negotiated during the connection establishment phase and remains active throughout the session, encrypting all data packets including authentication credentials, queries, result sets, and error messages. Azure SQL Database supports TLS versions 1.0, 1.1, and 1.2, with the recommendation to use TLS 1.2 or higher for optimal security as older versions have known vulnerabilities. Administrators can configure minimum TLS version requirements at the server level to ensure clients use secure protocol versions. Connection strings typically specify encryption settings, though modern Azure SQL Database connections encrypt by default. For enhanced security, organizations should verify that client applications are configured to validate server certificates, preventing man-in-the-middle attacks, use strong cipher suites for encryption, disable older TLS versions that have security weaknesses, and implement certificate pinning for highly sensitive applications. TLS encryption operates transparently to applications with minimal performance impact, providing strong protection without requiring application code changes. The encryption protects data as it travels across networks including the internet, internal networks, and Azure’s infrastructure.
Option A, IPSec (Internet Protocol Security), is a network layer protocol suite that authenticates and encrypts IP packets, providing secure communication at the network level. While IPSec is used in VPN scenarios and can secure network traffic between entire networks or between clients and networks, it is not the protocol Azure SQL Database uses for database connection encryption. IPSec operates at a lower network layer than application protocols like TDS (Tabular Data Stream) used by SQL Server, making it inappropriate as the answer for database-specific transmission security.
Option C, SSH (Secure Shell), is a cryptographic network protocol primarily used for secure remote login and command execution on Unix and Linux systems. SSH provides encrypted communication channels for interactive shell sessions, file transfers, and port forwarding. While SSH is fundamental for secure administration of Linux-based systems and can create encrypted tunnels for various protocols, it is not the native encryption protocol for SQL Server or Azure SQL Database connections. Database connections use TLS encryption rather than SSH tunneling for secure communication.
Option D, PPTP (Point-to-Point Tunneling Protocol), is a legacy VPN protocol that creates encrypted tunnels for network traffic. PPTP was historically used for remote access VPN connections but has known security vulnerabilities and is considered obsolete by modern security standards. Azure has deprecated PPTP support in favor of more secure VPN protocols like IKEv2 and OpenVPN. PPTP is not used for database connection encryption and would not be an appropriate answer for securing Azure SQL Database client connections.
Question 54:
You have an Azure SQL Database that is experiencing performance issues. You need to identify queries that are waiting on locks for extended periods. Which dynamic management view (DMV) should you query?
A)dm_db_resource_stats
B)dm_exec_query_stats
C)dm_tran_locks
D)dm_os_wait_stats
Answer: C
Explanation:
Performance troubleshooting in Azure SQL Database often requires investigating locking and blocking issues that cause queries to wait and applications to experience slowdowns. Azure SQL Database provides dynamic management views (DMVs) that expose real-time information about database internals, with different DMVs serving specific diagnostic purposes. When specifically investigating lock-related wait issues, sys.dm_tran_locks provides detailed information about active locks in the database, making option C the correct answer.
The sys.dm_tran_locks dynamic management view returns information about currently active lock resources and lock requests in the database engine, showing which transactions hold locks on which resources, the lock mode (shared, exclusive, update, etc.), and which transactions are waiting for locks held by others. This DMV is essential for diagnosing blocking scenarios where one query holds a lock that another query is waiting to acquire, causing the waiting query to stall. Information returned includes the resource_type indicating what is locked (database, object, page, row, key), resource_description providing specific details about the locked resource, request_mode showing the lock type being requested, request_status indicating whether the lock is granted or waiting, and request_session_id identifying which session holds or requests the lock. Administrators typically query sys.dm_tran_locks in conjunction with other DMVs such as sys.dm_exec_requests to identify blocking sessions, sys.dm_exec_sql_text to see query text for blocking queries, and sys.dm_exec_sessions to get session information. A common diagnostic query joins these views to create a comprehensive blocking chain analysis showing head blockers, victims, and the queries involved. Understanding locking patterns helps administrators identify problematic queries that hold locks too long, missing indexes causing table scans with extensive locking, or application design issues leading to lock contention. Resolution strategies may include query optimization, index creation, transaction scope reduction, isolation level adjustments, or application architecture changes.
Option A, sys.dm_db_resource_stats, provides historical resource utilization statistics for an Azure SQL Database including CPU percentage, data IO percentage, log IO percentage, memory usage, and worker thread counts sampled approximately every 15 seconds. This DMV is valuable for understanding overall resource consumption patterns and identifying resource bottlenecks at the database level. While resource stats might show high CPU or IO that could be symptomatic of blocking issues, this DMV does not provide lock-specific information or identify which queries are waiting on locks. It shows resource utilization effects rather than lock-level root causes.
Option B, sys.dm_exec_query_stats, contains aggregated performance statistics for cached query plans including execution counts, total CPU time, total elapsed time, total logical reads, and total physical reads. This DMV is excellent for identifying resource-intensive queries and understanding query performance characteristics across multiple executions. While query stats might reveal queries with high elapsed time that could be caused by lock waits, this DMV does not specifically show lock information or blocking relationships. It provides query performance metrics rather than real-time lock status.
Option D, sys.dm_os_wait_stats, provides cumulative statistics about wait types encountered by threads executing in SQL Server, showing aggregate wait times across all sessions since server startup or statistics reset. This DMV reveals which wait types are consuming the most time, such as LCK_M_X for exclusive lock waits or PAGEIOLATCH_SH for IO waits. While wait stats provide valuable high-level insight into performance bottlenecks including lock-related waits, they show cumulative aggregated data rather than current lock details. For identifying specific queries waiting on specific locks in real-time, sys.dm_tran_locks is more appropriate than the aggregated wait statistics.
Question 55:
You are implementing security for an Azure SQL Database that will be accessed by multiple applications. You need to ensure each application uses a separate identity with minimal necessary permissions. Which Azure AD feature should you implement?
A) Azure AD users
B) Managed identities
C) Shared access signatures
D) SQL authentication
Answer: B
Explanation:
Modern cloud applications require secure authentication mechanisms that avoid hardcoded credentials and follow the principle of least privilege. Azure provides several authentication options for Azure SQL Database, but managed identities offer the most secure and manageable approach for application authentication, making option B the correct answer.
Managed identities for Azure resources provide Azure services with an automatically managed identity in Azure Active Directory that applications can use to authenticate to services supporting Azure AD authentication, including Azure SQL Database. This feature eliminates the need for credentials in code, configuration files, or connection strings, significantly reducing credential exposure risk. There are two types of managed identities: system-assigned identities which are created automatically and tied to a specific Azure resource’s lifecycle, being deleted when the resource is deleted, and user-assigned identities which are created as standalone Azure resources and can be assigned to multiple resources. When an application uses a managed identity to connect to Azure SQL Database, Azure handles authentication token acquisition and management automatically behind the scenes. The workflow involves the application requesting a token from the Azure Instance Metadata Service, Azure AD validating the identity and issuing a token, and the application presenting the token to Azure SQL Database for authentication. Security benefits include no credentials stored in code or configuration, automatic credential rotation handled by Azure, integration with Azure Role-Based Access Control, audit trails in Azure AD logs, and simplified credential management. Implementation involves enabling managed identity on the Azure resource such as App Service or Azure Function, creating a database user for the managed identity in Azure SQL Database, granting appropriate permissions to that database user, and configuring the application connection string to use Azure AD authentication. Each application should have its own managed identity following least privilege principles, receiving only the specific database permissions required for its functionality.
Option A, Azure AD users, represent human identities in Azure Active Directory and can be granted access to Azure SQL Database for interactive database access. While Azure AD users provide centralized identity management and single sign-on benefits, they are intended for human users rather than application service principals. Using Azure AD user accounts for application authentication would require sharing human credentials with applications or creating dedicated user accounts for each application, neither of which follows security best practices. Managed identities provide purpose-built service identity without requiring password management.
Option C, shared access signatures (SAS), are tokens that grant limited access to Azure Storage resources without exposing storage account keys. SAS tokens are specific to Azure Storage services like Blob Storage, File Storage, Queue Storage, and Table Storage, and are not used for Azure SQL Database authentication. While SAS provides granular permissions and time-limited access for storage resources, it is not applicable to database authentication scenarios. The question specifically addresses Azure SQL Database access, not storage access.
Option D, SQL authentication, uses traditional SQL Server username and password authentication stored directly in the database. While SQL authentication is supported in Azure SQL Database for backward compatibility and specific scenarios, it
Retry
A
Continue
requires managing credentials that must be stored somewhere accessible to applications, typically in configuration files, environment variables, or key vaults. SQL authentication does not provide the automated credential management, rotation, and Azure integration benefits of managed identities. For new cloud-native applications, managed identities represent a more secure alternative that eliminates password management overhead.
Question 56:
You need to monitor and audit all database activities in an Azure SQL Database for compliance requirements. The solution must track both successful and failed access attempts and retain logs for one year. Which feature should you implement?
A) Azure SQL Database auditing
B) Query Performance Insight
C) Extended Events
D) SQL Server Profiler
Answer: A
Explanation:
Compliance and regulatory requirements often mandate comprehensive auditing of database activities to track who accessed what data and when, detect suspicious activities, and maintain audit trails for forensic investigation. Azure SQL Database provides native auditing capabilities specifically designed for these requirements, making option A the correct answer.
Azure SQL Database auditing tracks database events and writes them to an audit log stored in Azure Storage, Log Analytics workspace, or Event Hubs, providing comprehensive visibility into database activities. The auditing feature captures various events including data access and modifications, schema changes, authentication attempts both successful and failed, permission changes, backup and restore operations, and database operations. Audit logs record detailed information such as the action performed, timestamp, user or application identity, target object names, query statements executed, IP addresses, and operation results. Auditing can be configured at the server level to automatically apply to all existing and future databases on that server, or at the individual database level for specific databases requiring different policies. Organizations can select which event categories to audit, balancing compliance requirements against log volume and storage costs. For the one-year retention requirement specified in the question, administrators would configure audit log retention in Azure Storage with appropriate retention policies, or send logs to Log Analytics where retention is independently configurable. Common compliance frameworks requiring auditing include GDPR, HIPAA, PCI DSS, and SOX, each with specific audit trail requirements. Azure SQL Database auditing integrates with Azure Security Center and Azure Sentinel for advanced threat detection and security information and event management. Best practices include enabling auditing on all production databases, configuring appropriate retention periods, restricting access to audit logs, regularly reviewing audit data for anomalies, and testing log retention and retrieval processes periodically.
Option B, Query Performance Insight, is a performance monitoring tool that analyzes query execution and resource consumption to help identify performance bottlenecks. While Query Performance Insight tracks query execution history, it is designed for performance optimization rather than security auditing or compliance. It does not capture authentication attempts, permission changes, or many other security-relevant events required for comprehensive compliance auditing. Additionally, its retention period and data focus are geared toward performance analysis rather than long-term audit trail preservation.
Option C, Extended Events, is a lightweight performance monitoring system built into SQL Server and Azure SQL Database that allows capturing detailed diagnostic information about database activities. Extended Events provides extensive flexibility in defining which events to capture and can track virtually any database activity with minimal performance impact. However, Extended Events requires manual configuration of sessions, event selection, and target destinations, making it more complex than Azure SQL Database auditing for compliance scenarios. While technically capable of capturing required audit data, Extended Events lacks the compliance-focused design, retention management, and integration with Azure security services provided by the native auditing feature.
Option D, SQL Server Profiler, is a legacy monitoring tool traditionally used for SQL Server performance troubleshooting and trace analysis on on-premises SQL Server installations. SQL Server Profiler is not available for Azure SQL Database because it requires desktop client software connecting directly to the database engine with high privileges. Even where available, Profiler creates significant performance overhead and is being deprecated in favor of Extended Events. For cloud databases and modern monitoring scenarios, Azure SQL Database auditing or Extended Events are appropriate tools rather than SQL Server Profiler.
Question 57:
You are designing a multi-tenant SaaS application using Azure SQL Database where each tenant’s data must be isolated. You need to implement a solution that provides strong isolation while minimizing cost and management overhead. Which multi-tenancy model should you use?
A) Separate database per tenant
B) Shared database with schema per tenant
C) Shared database with shared schema using tenant ID column
D) Elastic pool with database per tenant
Answer: D
Explanation:
Multi-tenant application architecture involves critical decisions about data isolation, resource allocation, and cost optimization. Azure SQL Database supports multiple tenancy models, each with different tradeoffs regarding isolation, cost, scalability, and management complexity. When strong isolation is required while minimizing cost and management overhead, elastic pools with a database per tenant provide the optimal balance, making option D the correct answer.
Elastic pools are a cost-effective resource management feature that allows multiple Azure SQL databases to share a pool of resources (DTUs or vCores) with each database having dedicated storage but sharing compute capacity. In the database-per-tenant model within an elastic pool, each tenant receives a dedicated database providing strong isolation where tenant data is completely separated, schemas can be customized per tenant, performance of one tenant doesn’t directly impact others, and backup and restore operations are tenant-specific. The elastic pool provides cost optimization through resource sharing where tenants with different usage patterns share a resource pool, allowing some tenants to use more resources during peak times while others use less, reducing overall costs compared to provisioning dedicated resources for each database. Management overhead is reduced through pool-level configuration of performance tiers, backup policies, and monitoring, while still maintaining per-database isolation. This model accommodates varying tenant sizes by allowing different databases to consume different amounts of pool resources based on demand, and supports tenant mobility by enabling easy movement of tenant databases between pools or to standalone configurations if needed. Additional benefits include simplified compliance where tenant isolation is required, straightforward tenant onboarding and offboarding through database creation and deletion, and granular performance monitoring per tenant. The elastic pool model scales effectively for hundreds or even thousands of tenants depending on resource requirements and pool sizing.
Option A, separate database per tenant without elastic pools, provides the same strong isolation benefits but at significantly higher cost because each database must be provisioned with dedicated resources whether fully utilized or not. Without resource pooling, organizations cannot take advantage of usage pattern variations across tenants to optimize costs. This model becomes prohibitively expensive as tenant count grows, especially with many small tenants that individually use minimal resources but collectively require substantial total capacity. The management overhead is also higher with many independently configured databases.
Option B, shared database with schema per tenant, uses a single database with separate schemas for each tenant, providing moderate isolation where tenant tables exist in separate namespaces but share database resources and connection pools. While this reduces costs compared to separate databases, it provides weaker isolation because all tenants share the same database instance, making cross-tenant data access possible if row-level security or application logic fails. Performance issues affecting one tenant can impact all tenants sharing the database, and backup and restore operations are all-or-nothing for the entire database rather than tenant-specific. The management complexity of maintaining hundreds or thousands of schemas in a single database can become substantial.
Option C, shared database with shared schema using tenant ID column, provides the lowest isolation level where all tenants share the same tables with a tenant identifier column distinguishing tenant data. This approach minimizes infrastructure costs and management overhead but requires robust application logic and row-level security to prevent cross-tenant data access. Security risks are highest in this model because programming errors or SQL injection vulnerabilities could expose all tenant data. Performance impacts are shared across all tenants, and customization per tenant is extremely limited. This model is only appropriate where isolation requirements are minimal and cost optimization is the primary concern.
Question 58:
You have deployed an Azure SQL Database using the General Purpose service tier. The application requires sub-millisecond latency for transaction log writes. Which service tier should you migrate to in order to meet this requirement?
A) Standard tier (DTU-based)
B) Business Critical tier
C) Hyperscale tier
D) Serverless compute tier
Answer: B
Explanation:
Azure SQL Database offers multiple service tiers architected with different storage configurations, redundancy models, and performance characteristics designed for various workload requirements. When applications require extremely low-latency transaction log writes, the underlying storage architecture becomes critical, with the Business Critical tier providing the only option meeting sub-millisecond latency requirements, making option B the correct answer.
The Business Critical service tier uses a fundamentally different architecture compared to General Purpose, leveraging locally attached SSD storage rather than remote storage for data and log files. This architecture provides extremely low latency for IO operations including transaction log writes, which directly impacts transaction commit times and overall application responsiveness for write-heavy workloads. Business Critical tier features include local SSD storage providing sub-millisecond latency, built-in high availability through Always On availability groups with multiple synchronous replicas, one free readable secondary replica for read scale-out scenarios, zone redundancy option for protection against zone-level failures, and higher IO throughput compared to General Purpose tier. The availability group architecture maintains three to four total replicas depending on zone redundancy configuration, with one primary replica handling read-write operations and synchronous replication to secondary replicas ensuring data durability. The synchronous replication does not significantly impact write latency because it occurs in parallel with local writes to the primary replica’s SSDs. For applications with demanding performance requirements such as high-volume OLTP systems, financial trading platforms, or real-time analytics, Business Critical tier provides the necessary performance characteristics. The tier supports all database sizes and vCore configurations, allowing precise resource allocation based on workload requirements. Business Critical tier costs more than General Purpose due to the premium storage architecture and included readable replicas, but the performance benefits justify the cost for latency-sensitive applications.
Option A, Standard tier (DTU-based), uses the same remote storage architecture as General Purpose tier in the vCore model, where data and log files reside on Azure Premium Storage accessed over the network. While Premium Storage provides good performance for many workloads, network traversal introduces latency measured in milliseconds rather than sub-millisecond, making it unsuitable for applications requiring the lowest possible transaction log write latency. Standard tier is cost-effective for moderate workloads but cannot meet extreme latency requirements.
Option C, Hyperscale tier, is designed for very large databases up to 100 TB and provides rapid scale-up/down and fast backup/restore through innovative multi-tiered storage architecture. Hyperscale uses remote storage layers similar to General Purpose tier and does not provide the sub-millisecond transaction log write latency of Business Critical. While Hyperscale offers unique capabilities for massive databases and includes multiple readable replicas, its storage architecture is optimized for scale rather than lowest-possible latency. Applications requiring both massive scale and lowest latency would need to evaluate whether Hyperscale’s performance meets requirements or if alternative architectures are needed.
Option D, serverless compute tier, is a billing and scaling option within the General Purpose service tier that automatically pauses during inactivity and scales compute based on demand. Serverless uses the same remote storage architecture as provisioned General Purpose tier and therefore has the same latency characteristics measured in milliseconds rather than sub-millisecond. Serverless is designed for intermittent workloads where cost optimization is prioritized over lowest possible latency. Applications requiring sub-millisecond transaction log write latency would not meet their performance requirements with serverless compute.
Question 59:
You need to implement a disaster recovery solution for an Azure SQL Database that minimizes data loss in the event of a regional failure. The RTO (Recovery Time Objective) is 1 hour and the RPO (Recovery Point Objective) is 5 minutes. Which solution meets these requirements?
A) Point-in-time restore with geo-redundant backup
B) Active geo-replication with manual failover
C) Auto-failover groups with automatic failover policy
D) Zone-redundant configuration
Answer: C
Explanation:
Disaster recovery planning requires defining recovery time objectives (RTO) specifying maximum acceptable downtime and recovery point objectives (RPO) specifying maximum acceptable data loss. Azure SQL Database provides multiple DR options with different RTO and RPO characteristics. When requirements specify low RTO and RPO with regional failure protection, auto-failover groups with automatic failover policy provide the most appropriate solution, making option C the correct answer.
Auto-failover groups with automatic failover policy meet the specified requirements by providing RPO near zero (typically seconds to minutes of potential data loss) through continuous asynchronous replication to a secondary region, and RTO typically within seconds to minutes through automatic failover when the primary region becomes unavailable. The automatic failover policy monitors database availability and automatically initiates failover when connectivity to the primary region is lost for a configurable grace period, eliminating manual intervention delays. The grace period is configurable but typically set to one hour or less, ensuring RTO requirements are met. Key features enabling these recovery objectives include continuous data replication with minimal lag between primary and secondary regions, automatic failover without requiring administrator intervention, listener endpoints that automatically redirect applications to the current primary after failover, and support for multiple databases in coordinated failover. The replication lag (RPO) depends on transaction volume and network conditions but typically ranges from seconds to low minutes, comfortably meeting the 5-minute RPO requirement. After failover completes, applications automatically reconnect to the new primary region through the listener endpoint without requiring connection string changes or configuration updates. When the original primary region recovers, auto-failover groups support failback to restore the original configuration. Configuration involves creating a failover group between primary and secondary logical servers, adding databases to the group, configuring the automatic failover policy with appropriate grace period, and updating application connection strings to use listener endpoints rather than specific server names.
Option A, point-in-time restore with geo-redundant backup, provides protection against data corruption and regional failures by storing backups in a paired Azure region, enabling geo-restore to create a new database in an alternate region. However, geo-restore typically requires 20 minutes to several hours depending on database size because it involves copying and restoring from backup storage, making it unsuitable for the 1-hour RTO requirement. Additionally, RPO is determined by backup frequency rather than continuous replication, with potential data loss up to the time since the last transaction log backup was replicated to the secondary region, potentially exceeding the 5-minute RPO requirement during periods of high transaction activity.
Option B, active geo-replication with manual failover, provides continuous replication to secondary regions with low RPO similar to auto-failover groups, typically achieving RPO of seconds to minutes. However, manual failover requires administrator detection of the outage, decision to failover, and manual initiation of the failover process, introducing variable RTO that depends on monitoring, alerting, and human response time. During major regional outages affecting multiple systems simultaneously, administrator attention may be divided across multiple incidents, potentially delaying failover beyond the 1-hour RTO requirement. Manual failover also requires updating application connection strings or DNS to point to the new primary region, adding additional time and complexity to the recovery process.
Option D, zone-redundant configuration, provides high availability within a single Azure region by distributing database replicas across multiple availability zones within that region, protecting against datacenter-level failures. Zone redundancy provides excellent availability with automatic failover between zones and near-zero RPO and RTO for zone-level failures. However, zone-redundant configuration does not protect against regional outages affecting an entire Azure region simultaneously. The question specifically addresses regional failure scenarios, which require geo-replication to a secondary region rather than zone redundancy within a single region.
Question 60:
You are configuring firewall rules for an Azure SQL Database logical server. The database needs to be accessible from your company’s on-premises network with IP range 203.0.113.0/24 and from Azure services. Which firewall configuration should you implement?
A) Create a server-level firewall rule for 203.0.113.0-203.0.113.255 and enable «Allow Azure services and resources to access this server»
B) Create a database-level firewall rule only
C) Configure a virtual network service endpoint only
D) Disable the firewall completely
Answer: A
Explanation:
Network security is a fundamental aspect of Azure SQL Database administration, and properly configured firewall rules control which IP addresses and Azure services can connect to the database server. Azure SQL Database provides server-level and database-level firewall rules as the primary network access control mechanism. For the scenario requiring access from a specific IP range and from Azure services, server-level firewall rules with Azure services access enabled provide the appropriate solution, making option A the correct answer.
Server-level firewall rules apply to all databases on the logical server and are managed through the Azure portal, PowerShell, Azure CLI, or REST API. Creating a server-level rule for the IP range 203.0.113.0-203.0.113.255 allows all addresses within the company’s on-premises network to connect to any database on that logical server. The IP range represents a /24 subnet containing 256 addresses from 203.0.113.0 through 203.0.113.255, requiring a firewall rule specifying the start and end addresses of this range. Additionally, enabling «Allow Azure services and resources to access this server» permits Azure services such as Azure App Service, Azure Functions, Azure Data Factory, and other Azure resources to connect to the database, which is essential for cloud-native applications hosted in Azure that need database access. This setting creates a special rule allowing connections from Azure’s IP address space without requiring explicit rules for every possible Azure service IP. The combination provides access for both on-premises users and Azure-hosted applications without overly permissive configuration. Best practices for firewall management include using the minimum necessary IP ranges rather than broad ranges, documenting the purpose of each rule, regularly reviewing and removing obsolete rules, considering virtual network rules for enhanced security over IP-based rules where possible, and combining firewall rules with authentication for defense in depth. Firewall rules should be tested after creation to verify connectivity from intended sources and blocked access from unauthorized sources.
Option B, creating a database-level firewall rule only, would provide access to a single specific database rather than server-wide access, which could be appropriate in some scenarios. However, database-level rules are managed through T-SQL rather than the Azure portal and must be created separately for each database requiring the rule. Additionally, database-level rules cannot enable the «Allow Azure services» setting, which must be configured at the server level. For scenarios requiring Azure service access and access to multiple databases, server-level rules are more appropriate than database-level rules.
Option C, configuring a virtual network service endpoint only, provides enhanced security by extending Azure virtual network private address space to the database server, allowing traffic from specific VNets and subnets without traversing the public internet. Virtual network rules are more secure than IP-based rules and are recommended for Azure-hosted applications. However, VNet service endpoints only work for traffic originating from Azure virtual networks, not for on-premises networks accessing over the internet. The question specifies on-premises network access from a public IP range, which requires IP-based firewall rules rather than or in addition to VNet rules. If the on-premises network connected via ExpressRoute or VPN Gateway with VNet integration, VNet rules could be part of the solution, but the question describes public IP-based access.
Option D, disabling the firewall completely, would allow unrestricted access from any IP address on the internet to the database server, creating an enormous security risk. Azure SQL Database firewall should never be completely disabled for production databases as it provides the first line of defense against unauthorized access attempts. Even with strong authentication, exposing database endpoints to the entire internet invites credential attacks, vulnerability exploitation, and compliance violations. Firewall rules should implement the principle of least privilege by allowing only necessary source addresses to connect to the database.