Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 1 Q1-15
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Question 1
A company is running a critical e-commerce application in AWS. The application needs to handle unpredictable traffic spikes, and any downtime will result in revenue loss. Which AWS service combination should the solutions architect recommend to achieve high availability and scalability?
A) EC2 instances in a single Availability Zone with Auto Scaling
B) EC2 instances in multiple Availability Zones behind an Application Load Balancer with Auto Scaling
C) Lambda functions in a single region with S3 as the only storage
D) EC2 instances with Spot Instances only in one Availability Zone
Answer: B) EC2 instances in multiple Availability Zones behind an Application Load Balancer with Auto Scaling
Explanation:
Running EC2 instances in a single Availability Zone with Auto Scaling improves scalability, but if the Availability Zone fails, the application becomes unavailable, making it unsuitable for critical applications. Lambda functions in a single region can scale automatically, but without a multi-AZ strategy and proper state management, they may not meet high availability requirements. EC2 instances using Spot Instances in one Availability Zone are cost-efficient, but Spot Instances can be terminated unexpectedly, and being in a single AZ introduces a single point of failure. Deploying EC2 instances across multiple Availability Zones behind an Application Load Balancer allows automatic distribution of traffic across healthy instances in different AZs, ensuring resilience against zone failures. Auto Scaling adjusts the number of instances according to traffic patterns, enabling the system to handle spikes without manual intervention. This combination ensures both high availability and elasticity, meeting the company’s requirements.
Question 2
A company wants to store large amounts of frequently accessed and infrequently accessed data with low latency and durability. Which AWS storage solution should a solutions architect recommend?
A) S3 Standard and S3 Glacier
B) S3 Standard and S3 Intelligent-Tiering
C) EBS gp3 volumes only
D) EFS Standard only
Answer: B) S3 Standard and S3 Intelligent-Tiering
Explanation:
S3 Standard is ideal for frequently accessed data due to low latency and high durability but is more expensive than infrequent storage classes. S3 Glacier is used for archival purposes, which introduces retrieval delays and is not suitable for frequently accessed data. EBS gp3 volumes provide block storage for EC2 instances, but managing large datasets across multiple instances can be complex and costly compared to S3. EFS Standard offers shared file storage with high availability but is optimized for NFS workloads rather than object storage and may not be cost-effective for large datasets with mixed access patterns. S3 Intelligent-Tiering automatically moves objects between access tiers based on access patterns, reducing costs for infrequently accessed data while maintaining low latency for active data. Combining S3 Standard for frequently accessed objects with Intelligent-Tiering for less frequently used objects provides cost optimization, durability, and low latency, fulfilling the company’s requirements.
Question 3
A solutions architect is designing a system that processes sensitive financial transactions. The company requires that all data at rest and in transit must be encrypted. Which AWS services and features satisfy these requirements?
A) S3 with SSE-S3, HTTPS, and KMS for key management
B) S3 with SSE-C only
C) EC2 with unencrypted EBS volumes and VPC peering
D) DynamoDB without encryption
Answer: A) S3 with SSE-S3, HTTPS, and KMS for key management
Explanation:
Ensuring data security in the cloud requires careful consideration of both storage and transmission safeguards. Simply storing data in AWS services without proper encryption can leave sensitive information exposed, potentially violating regulatory requirements and increasing the risk of data breaches. Various AWS services offer encryption capabilities, but their effectiveness depends on how encryption keys are managed and whether encryption is applied consistently for both data at rest and in transit.
For example, S3 supports client-provided keys through Server-Side Encryption with Customer-Provided Keys (SSE-C). While this method does provide encryption at rest, it shifts the responsibility of key management entirely to the customer. Managing encryption keys manually introduces additional complexity, operational overhead, and potential security risks. Keys must be securely stored, rotated, and protected from unauthorized access, and any lapse in key management could result in data loss or compromise.
Similarly, using EC2 instances with unencrypted EBS volumes does not satisfy data-at-rest encryption requirements. While EC2 provides flexibility for compute and storage, unencrypted volumes leave stored data vulnerable if the underlying hardware is accessed by unauthorized users. VPC peering, on the other hand, is a networking construct that allows private connectivity between virtual networks but does not encrypt data traversing the connection. Without additional encryption mechanisms, data in transit remains unprotected, increasing exposure to interception.
DynamoDB without encryption also poses risks for sensitive data, as the database stores information in plaintext. In scenarios where compliance with industry regulations or protection of sensitive financial or personal data is required, relying on unencrypted storage is inadequate. Any solution must ensure that all data at rest is encrypted with robust algorithms and secure key management.
A more secure and manageable approach involves using S3 with server-side encryption, specifically SSE-S3 or SSE-KMS. SSE-S3 automatically encrypts data at rest using keys managed by AWS, providing strong protection without burdening the customer with key handling responsibilities. For greater control and compliance, AWS Key Management Service (KMS) can be integrated with S3 to provide centralized key management. KMS allows organizations to create, rotate, and revoke encryption keys, enabling fine-grained control over access while ensuring that encryption practices meet organizational and regulatory standards. KMS also provides audit logging for key usage, supporting compliance and governance requirements.
Equally important is securing data in transit. Using HTTPS to encrypt communication between clients and AWS services ensures end-to-end encryption, protecting data as it moves across the network. This combination of at-rest encryption with SSE-S3 or SSE-KMS and in-transit encryption via HTTPS delivers comprehensive protection for sensitive information, including financial transaction data.
relying on services without integrated encryption or customer-managed keys introduces complexity, risk, and potential non-compliance. Implementing S3 with server-side encryption combined with AWS KMS provides centralized, automated key management, while HTTPS ensures secure transport. This architecture addresses both data-at-rest and data-in-transit requirements, delivering a robust, scalable, and regulatory-compliant solution for protecting sensitive data in the cloud.
Question 4
A company is deploying a web application that must maintain session state across multiple servers. Which AWS service is best suited to store session data with low latency?
A) RDS MySQL
B) DynamoDB
C) ElastiCache Redis
D) S3
Answer: C) ElastiCache Redis
Explanation:
Selecting the right storage solution for session state in web applications is crucial to ensure performance, scalability, and reliability. Session data typically requires extremely low-latency access, as it is frequently read and updated during user interactions. Choosing an inappropriate storage system can introduce delays, reduce application responsiveness, and negatively impact user experience. While AWS provides several database and storage options, not all are suitable for high-speed session management.
Amazon RDS MySQL is a fully managed relational database service that provides durable, structured storage and supports ACID-compliant transactions. While RDS MySQL is excellent for transactional applications and persistent relational data, it introduces latency when used for frequent session reads and writes. Every interaction requires a network round-trip to the database and query execution overhead, which can become a performance bottleneck for applications with a high volume of short-lived, rapidly changing session data. This makes RDS less ideal for workloads that demand microsecond-level response times for session management.
DynamoDB is a NoSQL database designed for high scalability and low operational overhead. It can handle large volumes of reads and writes and provides single-digit millisecond latency under typical workloads. However, even DynamoDB’s performance, while fast, may not consistently meet the extreme low-latency requirements needed for real-time session data, especially under very high request rates. Additionally, DynamoDB does not provide in-memory caching by default, so each session read or update requires access to persistent storage, introducing additional latency compared to an in-memory solution.
Amazon S3, as an object storage service, is excellent for storing large, static files such as media assets, backups, or logs. However, S3 is not designed for rapid read/write cycles or mutable data. Its storage model and access patterns make it unsuitable for session state, which requires frequent updates and immediate retrieval, as S3 operations are optimized for throughput and durability rather than low-latency access.
ElastiCache with Redis provides a high-performance, in-memory data store that is specifically designed for workloads requiring extremely low-latency access. Because Redis stores data in memory rather than on disk, read and write operations can be completed in microseconds. This makes it ideal for session management, where rapid retrieval and updates are critical to maintain application responsiveness. Redis supports advanced features such as data persistence, replication, clustering, and high availability, ensuring that session data remains consistent across multiple web servers and providing resilience against node failures. By using Redis clusters, applications can scale horizontally to handle increased traffic while maintaining low latency for session access.
Redis also allows complex data structures such as hashes, lists, and sets, which are particularly useful for storing session attributes and managing user state efficiently. Combined with its built-in support for automatic failover and replication, ElastiCache Redis provides both speed and reliability, fulfilling the dual requirements of performance and availability for session data storage.
In summary, while RDS MySQL, DynamoDB, and S3 each offer value in specific use cases, they do not meet the stringent low-latency requirements of session management. ElastiCache Redis, with its in-memory architecture, replication, clustering, and high availability features, provides an optimal solution for storing session state. It ensures extremely fast access to frequently changing data while maintaining consistency across multiple servers, making it the ideal choice for applications that depend on high-performance session handling.
Question 5
A company needs to migrate a multi-terabyte database to AWS with minimal downtime. Which migration approach should the solutions architect recommend?
A) Export the database to S3, then import into RDS
B) Use AWS Database Migration Service (DMS) with continuous replication
C) Perform a full dump and restore during a maintenance window
D) Use manual SQL scripts to insert all data
Answer: B) Use AWS Database Migration Service (DMS) with continuous replication
Explanation:
Migrating a database to AWS can be a complex task, especially when minimizing downtime is a priority. Traditional methods, such as exporting a database to S3 and importing it into Amazon RDS, often introduce significant operational challenges. Exporting a full database, transferring it to cloud storage, and then importing it into RDS requires a maintenance window during which the source database may be unavailable. This approach can lead to extended downtime, disrupting critical business operations, particularly for applications that rely on real-time access to the database. Moreover, performing a full dump and restore is often inefficient for large-scale databases and can be prone to errors if any step in the process fails.
Another common strategy involves using manual SQL scripts to extract data from the source database and insert it into the target AWS database. While this method may work for small datasets or non-critical systems, it becomes increasingly error-prone and time-consuming as database size grows. Manual intervention increases the risk of missing records, introducing inconsistencies, or failing to replicate complex schema objects such as triggers, stored procedures, or constraints. Additionally, maintaining application availability during a manual migration is challenging because the source database may need to be taken offline to ensure data consistency, further increasing downtime.
AWS Database Migration Service (DMS) provides a more efficient, reliable, and operationally safe approach to database migration. DMS enables continuous replication from on-premises databases or databases hosted in other clouds to AWS. With DMS, the source database can remain fully operational while data is migrated incrementally, reducing downtime to a minimum. Instead of relying on a single snapshot or bulk transfer, DMS continuously captures changes made to the source database and applies them to the target database in near real-time. This allows applications to continue operating with minimal disruption throughout the migration process.
DMS also offers extensive support for schema and data transformation, simplifying the migration of heterogeneous databases. For example, it can convert data types and map schemas from one database engine to another, allowing migrations between different platforms such as Oracle to Amazon Aurora or SQL Server to PostgreSQL. This flexibility eliminates the need for complex manual conversion scripts and reduces the risk of errors. Additionally, DMS integrates with other AWS services such as S3, CloudWatch, and Lambda, enabling monitoring, alerting, and automated workflows during migration.
Using DMS, organizations can plan a phased migration, performing incremental updates to keep the source and target databases synchronized. Once the target database is fully up-to-date and validated, a smooth cutover can be performed with minimal downtime. This approach ensures that business operations continue with little interruption, while IT teams can verify that the migration is complete and accurate before decommissioning the old system.
exporting and importing databases manually or using SQL scripts is cumbersome, error-prone, and leads to unnecessary downtime, especially for large or complex datasets. AWS Database Migration Service provides a modern, automated solution for both homogeneous and heterogeneous migrations, supporting continuous replication and minimizing disruption. Its capabilities make it the preferred choice for organizations seeking a seamless, reliable, and efficient path to migrate databases to the AWS environment while maintaining high availability and operational continuity.
Question 6
A company wants to reduce costs by automatically stopping non-production EC2 instances outside working hours. Which solution provides the most effective cost optimization?
A) AWS Auto Scaling with a scheduled action
B) AWS Systems Manager Automation with a cron schedule
C) Manual stopping of instances by administrators
D) Create EC2 Spot Instances only
Answer: B) AWS Systems Manager Automation with a cron schedule
Explanation:
Auto Scaling can terminate and launch instances, but stopping instances based on a fixed schedule for non-production environments is better handled by Systems Manager Automation. Manual stopping of instances is labor-intensive, prone to human error, and unreliable. Using only Spot Instances reduces cost but does not guarantee availability, making it unsuitable for scheduled non-production workloads. Systems Manager Automation allows scheduling the start and stop of EC2 instances with cron expressions, ensuring that instances run only during required hours, automatically reducing operational costs without affecting productivity. It can also include notifications and logging for audit purposes, providing an effective and reliable cost optimization mechanism.
Question 7
A solutions architect must design a secure API layer for a mobile application. The APIs should scale automatically and protect against common web attacks. Which AWS service combination is most appropriate?
A) API Gateway with AWS WAF and Lambda
B) API Gateway with S3 hosting
C) Lambda only without API Gateway
D) EC2 instances with Nginx
Answer: A) API Gateway with AWS WAF and Lambda
Explanation:
When building APIs on AWS, it is essential to select a solution that balances scalability, security, cost-efficiency, and ease of management. While there are multiple approaches for delivering content and executing backend logic, not all options provide the comprehensive features required for enterprise-grade API management. Services like S3, Lambda, and EC2 each address specific use cases but have limitations when it comes to building dynamic, secure, and scalable APIs.
Amazon S3 can host static content efficiently and serve files directly to users with high availability and low latency. While this approach is excellent for static websites or static API documentation, it does not natively support dynamic request processing, authentication, or complex business logic. Using S3 alone cannot accommodate APIs that need real-time data processing, user-specific responses, or controlled access, which are often essential for modern web applications.
AWS Lambda provides a serverless compute layer capable of executing backend logic without the need to manage servers. Lambda functions scale automatically with incoming requests and can run code in response to events, making them highly efficient for processing dynamic API calls. However, Lambda alone does not provide API management capabilities such as request routing, rate limiting, authentication, caching, or monitoring. Without an API management layer, developers must implement these features manually, increasing development and operational overhead.
Deploying APIs on EC2 instances using a web server such as Nginx offers significant flexibility. Developers have full control over the runtime environment, middleware, and networking configuration, allowing them to implement any desired functionality. However, this approach requires manual management of compute resources, including scaling to handle variable traffic, patching operating systems, applying security updates, and configuring load balancing. This operational burden can increase costs and complicate maintenance, especially for applications with fluctuating workloads.
Amazon API Gateway provides a fully managed solution for creating, deploying, and managing APIs at any scale. It allows developers to define RESTful or WebSocket APIs, route requests to Lambda functions or other backends, and enforce security policies. API Gateway automatically scales with traffic, removing the need for manual provisioning of compute resources and ensuring consistent performance under heavy load. It also provides built-in features such as throttling, request validation, caching, and authorization, which enhance both performance and security without additional custom implementation.
Integrating API Gateway with Lambda creates a powerful serverless architecture where dynamic API requests are processed efficiently, securely, and cost-effectively. To further strengthen security, AWS Web Application Firewall (WAF) can be deployed alongside API Gateway. WAF protects APIs against common web exploits, including SQL injection, cross-site scripting, and other application-layer attacks. This combination ensures that APIs are resilient, compliant, and capable of supporting enterprise-grade traffic patterns.
In summary, while S3, Lambda, and EC2 each provide valuable capabilities, they are limited when used in isolation for dynamic API workloads. API Gateway, combined with Lambda and optionally secured with AWS WAF, provides a fully managed, scalable, and secure solution for building modern APIs. This architecture allows organizations to deliver reliable, high-performance APIs while minimizing operational overhead and maintaining strong security posture, making it the ideal choice for enterprise applications.
Question 8
A company needs a highly available and cost-effective data warehouse solution for analytics. Which AWS service best meets these requirements?
A) Amazon Redshift with single-node cluster
B) Amazon Redshift with RA3 nodes and Concurrency Scaling
C) RDS MySQL with Multi-AZ
D) S3 with Athena only
Answer: B) Amazon Redshift with RA3 nodes and Concurrency Scaling
Explanation:
When designing a data warehousing solution in AWS, it is crucial to consider both performance and reliability. While Amazon Redshift, RDS MySQL, and S3 with Athena all provide mechanisms to store and query data, their suitability varies significantly depending on workload requirements, dataset size, and availability needs. Selecting the right architecture can directly impact query efficiency, scalability, and cost-effectiveness, particularly for enterprise-level analytics.
A single-node Redshift cluster is often the simplest way to get started with Redshift, but it comes with significant limitations. Since it has only one node, there is no redundancy, making it vulnerable to hardware failures or service interruptions. Any downtime directly impacts accessibility and reliability, which is unacceptable for critical workloads where high availability is essential. Additionally, single-node clusters may not provide the performance required for complex analytical queries or large datasets due to limited processing power and storage capacity.
RDS MySQL is another option for relational data storage, but it is primarily designed for transactional workloads rather than analytical workloads. While it can handle structured data efficiently and support standard SQL queries, MySQL does not scale well for large datasets or complex, multi-join queries typically found in business intelligence and analytics applications. Performing large-scale analytical processing on RDS MySQL often results in slow query performance and may require significant manual optimization or sharding, which adds operational complexity.
S3 combined with Athena offers a serverless approach for querying data without the need to manage infrastructure. Athena allows SQL queries directly on structured and semi-structured data stored in S3 and is cost-efficient for on-demand analytics. However, as dataset sizes grow into the terabytes or petabytes, query performance can become unpredictable. Complex queries on very large datasets may incur longer execution times, making it less suitable for workloads that require consistently low-latency responses or high concurrency for multiple users.
For enterprise-grade analytical workloads, Amazon Redshift RA3 nodes offer a superior solution. RA3 architecture separates storage and compute, enabling organizations to scale each independently. This separation allows storage to grow without affecting compute resources and ensures that analytical queries can be executed efficiently regardless of dataset size. Additionally, Redshift RA3 nodes reduce costs by allowing compute resources to scale to meet query demand without over-provisioning storage, providing a more predictable and optimized expenditure.
Redshift’s Concurrency Scaling feature further enhances performance by automatically adding temporary compute capacity when query loads spike. This ensures that even during periods of heavy usage, queries maintain consistent speed and reliability without manual intervention. Combined with high availability features such as managed snapshots, automated backups, and cross-node replication, RA3 clusters deliver both resilience and performance.
while single-node Redshift clusters, RDS MySQL, and S3 with Athena each offer useful capabilities, they fall short for high-performance, enterprise-scale analytics. Redshift RA3 nodes, with their decoupled storage and compute architecture and Concurrency Scaling, provide an optimal balance of cost efficiency, high availability, and reliable query performance. This makes RA3 the ideal choice for organizations looking to build robust, scalable, and efficient data warehousing solutions that can meet modern analytical demands.
Question 9
A company wants to deploy an application that processes high-throughput, low-latency streaming data. Which AWS service should the solutions architect recommend?
A) Kinesis Data Streams
B) SQS Standard Queue
C) SNS Topic
D) S3 Event Notifications
Answer: A) Kinesis Data Streams
Explanation:
When designing a solution for real-time data processing in AWS, selecting the right messaging and streaming service is crucial to ensure performance, scalability, and reliability. While AWS provides a variety of messaging and event-handling services, not all are suitable for high-throughput, low-latency stream processing scenarios. Services such as SQS, SNS, and S3 Event Notifications serve specific purposes but have limitations that make them less ideal for continuous, large-scale streaming workloads.
Amazon SQS Standard Queues offer reliable message delivery with at-least-once delivery guarantees and support for distributed, decoupled application architectures. SQS ensures that messages are stored durably and can be processed asynchronously by multiple consumers. However, SQS is designed primarily for queuing workloads where the rate of message production and consumption is moderate. When workloads require high-throughput or sub-second latency processing, SQS may not provide the necessary performance characteristics. It is optimized for reliability and durability rather than real-time streaming, making it less suited for applications that need immediate processing of continuously generated data.
Amazon SNS, or Simple Notification Service, is a pub/sub messaging service that enables applications to send notifications to multiple subscribers simultaneously. SNS is excellent for alerting, fan-out messaging, and integrating with other AWS services for event-driven architectures. Nevertheless, SNS is not designed for stream processing where ordered, continuous data flows need to be consumed by multiple applications concurrently. Its primary use case revolves around broadcasting messages to endpoints such as email, SMS, HTTP/S endpoints, or Lambda functions, rather than maintaining an ordered, high-volume stream for analytics or processing pipelines.
S3 Event Notifications allow S3 buckets to trigger actions when objects are created, modified, or deleted. This functionality is valuable for reactive workflows, such as invoking Lambda functions to process uploaded files. However, S3 Event Notifications are not optimized for continuous, high-throughput streams. They are limited in handling large volumes of small, rapidly arriving events, and do not provide features like partitioning or fine-grained concurrency control needed for real-time data streams.
Amazon Kinesis Data Streams is purpose-built for real-time, high-volume data streaming. Kinesis allows producers to continuously ingest massive amounts of data and enables multiple consumers to process these records concurrently with millisecond-level latency. Its partitioned shard architecture allows workloads to scale horizontally according to throughput demands, providing both high performance and fault tolerance. Kinesis supports multiple consumers reading from the same stream simultaneously, making it ideal for scenarios where different applications or analytics pipelines need access to the same dataset in real time. Additionally, it provides ordering guarantees within each shard, enabling complex event processing, real-time analytics, and immediate reaction to streaming events.
In summary, while SQS, SNS, and S3 Event Notifications each provide valuable messaging and event-handling capabilities, they are not designed for continuous, large-scale stream processing. SQS excels at reliable queuing, SNS at pub/sub notifications, and S3 Event Notifications at triggering workflows based on object events. For applications that demand real-time ingestion, low latency, high throughput, and concurrent processing by multiple consumers, Kinesis Data Streams is the optimal solution. Its scalability, millisecond-level responsiveness, and support for parallel processing make it the ideal service for building robust, real-time streaming architectures in AWS.
Question 10
A solutions architect must ensure that a multi-tier web application is fault-tolerant across multiple regions. Which AWS services help achieve this goal?
A) Route 53 with health checks, S3 Cross-Region Replication, and Multi-Region ALB
B) Route 53 with health checks, multi-region Auto Scaling groups, and S3 Cross-Region Replication
C) CloudFront with regional caching only
D) Single-region ALB with Auto Scaling
Answer: B) Route 53 with health checks, multi-region Auto Scaling groups, and S3 Cross-Region Replication
Explanation:
Route 53 with health checks alone cannot provide fault tolerance without multi-region resources. Multi-Region ALB is not natively supported; ALBs are regional, making cross-region ALB deployment impossible. CloudFront provides caching but does not make applications multi-region fault-tolerant. Single-region ALB with Auto Scaling ensures high availability in one region but fails in regional outages. Using Route 53 with health checks can route traffic to healthy regions. Multi-region Auto Scaling groups provide redundancy and fault tolerance, and S3 Cross-Region Replication ensures static assets are available globally. This combination ensures the application can withstand regional failures while serving users efficiently.
Question 11
A company needs to analyze large datasets with varying schema formats. Which AWS service is most appropriate for this requirement?
A) Amazon Redshift
B) Amazon Athena
C) Amazon RDS PostgreSQL
D) Amazon DynamoDB
Answer: B) Amazon Athena
Explanation:
When choosing a data analytics solution in AWS, understanding how each service handles data structure and querying is critical. Amazon Redshift, for example, is a powerful data warehouse optimized for structured data. It requires well-defined schemas, making it highly effective for consistent, relational datasets where the structure is known in advance. While Redshift excels at performing large-scale analytical queries on structured data, its reliance on fixed schemas limits flexibility when working with datasets that have irregular or evolving structures. Any change in the data format often requires schema modifications, which can slow down analysis and increase administrative overhead.
Similarly, RDS PostgreSQL is a relational database that also demands predefined schemas. It is designed to store structured data and enforce relationships between tables, which is ideal for transactional workloads and applications that require consistency and integrity. However, for scenarios where the data structure varies over time or where a schema-on-read approach is desired, PostgreSQL is less suited. Analysts or data scientists must first define the schema and transform incoming data to fit it before performing queries, adding extra steps and complexity for exploratory or ad-hoc analysis.
DynamoDB, a fully managed NoSQL service, provides high-performance key-value and document storage. It is excellent for workloads that require low-latency access to items based on specific keys or queries over limited indexes. While DynamoDB is highly scalable and resilient, it is not designed for complex analytical queries or joining large datasets. Performing ad-hoc analysis across semi-structured or large-scale datasets is challenging because the service does not support standard SQL querying and lacks built-in capabilities for handling relational joins or aggregations.
In contrast, Amazon Athena offers a flexible, serverless approach to querying data stored in S3. Athena allows users to run standard SQL queries directly against raw data without requiring a predefined schema. This schema-on-read capability is especially valuable for semi-structured or evolving datasets, as it enables analysts to define the schema dynamically at query time rather than enforcing it at ingestion. Athena supports a wide range of file formats, including CSV, Parquet, JSON, and ORC, making it suitable for heterogeneous datasets and large-scale analytics. Its serverless model eliminates the need to provision or manage infrastructure, allowing users to focus solely on querying and analyzing data.
Athena’s ability to handle ad-hoc queries efficiently makes it ideal for exploratory analytics. Analysts can interactively examine data, test hypotheses, and combine datasets in different formats without performing extensive pre-processing or transformations. Additionally, Athena integrates with the AWS Glue Data Catalog, providing a centralized metadata repository that enhances discoverability and simplifies schema management for large and diverse datasets.
Overall, while Redshift, RDS PostgreSQL, and DynamoDB each provide strong capabilities for structured and transactional workloads, Athena offers unmatched flexibility for querying semi-structured or evolving datasets. Its schema-on-read approach, broad file format support, and serverless execution make it particularly well-suited for ad-hoc analytics on data stored in S3. For organizations looking to perform fast, scalable, and flexible queries across diverse datasets, Athena provides a modern and efficient solution that bridges the gap between raw data storage and actionable insights.
Question 12
A company requires a hybrid architecture with low-latency access to on-premises data from AWS. Which AWS service provides the most suitable solution?
A) Direct Connect
B) VPN over the public internet
C) S3 Transfer Acceleration
D) AWS Snowball
Answer: A) Direct Connect
Explanation:
VPN over the public internet is simple but may have variable latency and bandwidth limitations. S3 Transfer Acceleration is optimized for fast uploads to S3 but does not provide low-latency hybrid access. AWS Snowball is for offline bulk data transfer, not real-time access. Direct Connect establishes a dedicated, private network connection from on-premises to AWS, providing consistent low latency, high bandwidth, and secure access to AWS services. It enables hybrid applications to access resources in AWS as if they were on the local network, making it the most suitable solution for low-latency hybrid architectures.
Question 13
A company wants to ensure compliance by logging all API activity across its AWS account. Which service provides this functionality?
A) CloudTrail
B) CloudWatch Logs
C) Config
D) GuardDuty
Answer: A) CloudTrail
Explanation:
In AWS, monitoring and auditing activities across resources is essential for maintaining security, ensuring compliance, and performing forensic analysis in the event of incidents. While AWS offers several services that provide visibility into resource behavior and operational events, not all of them capture comprehensive API activity. Services like CloudWatch Logs, AWS Config, and GuardDuty each address specific aspects of monitoring and security, but only CloudTrail provides a full, detailed record of API calls across an account.
CloudWatch Logs is primarily designed to collect, store, and analyze log data generated by AWS resources and applications. It enables real-time monitoring, troubleshooting, and alerting based on log patterns, making it useful for operational visibility. CloudWatch can aggregate logs from EC2 instances, Lambda functions, and other services, allowing administrators to track application behavior and performance. However, it does not record every API call made within an AWS account, so it cannot serve as a complete audit trail for actions performed by users, applications, or automated systems.
AWS Config provides a different type of monitoring, focusing on resource configurations and compliance. Config tracks changes to AWS resources and evaluates them against pre-defined rules to detect misconfigurations or drift from desired states. This makes it a valuable tool for compliance checks and ensuring infrastructure remains consistent with organizational policies. Although Config can alert on configuration changes, it does not capture the full context of API activity, such as who initiated a call, when it occurred, and what parameters were used. Therefore, Config alone cannot provide the detailed auditing required for forensic investigations or regulatory compliance.
GuardDuty offers threat detection by analyzing logs and network activity to identify suspicious behavior or potential security risks. It can detect compromised instances, unusual API calls, and other indicators of malicious activity. GuardDuty provides high-level alerts for security monitoring but is not intended to serve as a complete record of all user and service actions. Its focus is on identifying threats rather than documenting every API transaction within an account.
AWS CloudTrail, on the other hand, is specifically designed to capture a complete history of API calls across AWS services. CloudTrail records all interactions with AWS, whether initiated through the management console, SDKs, or CLI. Each log entry contains critical details such as the identity of the caller, the time of the request, the API action invoked, request parameters, and response elements. This level of detail makes CloudTrail an essential tool for auditing, compliance verification, and forensic analysis. By providing a comprehensive record of all activity, organizations can reconstruct events, investigate anomalies, and ensure accountability across accounts.
CloudTrail can be integrated with Amazon S3 for durable log storage and with CloudWatch for near real-time monitoring and alerting. This integration allows organizations to retain logs for long-term compliance, create dashboards, and trigger automated responses when unusual activity is detected. By combining CloudTrail with other monitoring and security services, AWS users gain a holistic view of both operational and security-relevant activity across their cloud environment.
while CloudWatch Logs, AWS Config, and GuardDuty provide valuable insights into resource health, configuration, and threats, AWS CloudTrail is the definitive solution for comprehensive API activity logging. It records every action taken in an account, supports auditing and compliance requirements, and integrates seamlessly with storage and monitoring services, providing a complete and reliable record of all AWS activities.
Question 14
A company wants to deploy a containerized application in AWS without managing servers. Which service should the solutions architect recommend?
A) ECS with Fargate
B) ECS with EC2 launch type
C) EKS with EC2 nodes
D) Lambda functions
Answer: A) ECS with Fargate
Explanation:
ECS with EC2 launch type requires managing EC2 instances, including scaling, patching, and capacity planning. EKS with EC2 nodes also requires managing Kubernetes nodes. Lambda functions are suitable for serverless workloads with limited runtime durations and are not ideal for long-running containerized applications. ECS with Fargate is a serverless compute engine for containers that removes the need to manage servers, automatically scales containers, and integrates with networking, logging, and security services. This solution allows running containerized workloads efficiently without operational overhead, meeting the company’s requirement for a fully managed, serverless container platform.
Question 15
A solutions architect needs to design a data lake on AWS that supports diverse analytics workloads, including machine learning and real-time analytics. Which combination of services is most appropriate?
A) S3, Glue, Athena, and SageMaker
B) S3, EC2, and RDS only
C) DynamoDB and Lambda
D) EFS and Redshift only
Answer: A) S3, Glue, Athena, and SageMaker
Explanation:
While basic AWS services such as S3, EC2, and RDS provide essential building blocks for storage and compute, they do not offer a comprehensive solution for modern analytics or machine learning workflows. These services are excellent for hosting applications and storing structured or unstructured data, but they lack the integrated capabilities needed for managing large-scale, diverse datasets, performing complex analytics, or building machine learning models. Organizations that rely solely on these services often face challenges in combining, transforming, and analyzing data efficiently.
Serverless options like DynamoDB and Lambda excel in specific scenarios but are not designed for complex analytics workloads. DynamoDB offers fast, scalable NoSQL storage, while Lambda allows event-driven compute without managing servers. However, neither service provides the advanced querying, ETL (extract, transform, load), or machine learning integration required for building a flexible analytics platform. These services are well suited for transactional or operational tasks, real-time event processing, and microservices, but they cannot replace a full-featured data lake capable of handling analytical and ML workloads at scale.
File storage and data warehousing services such as EFS and Redshift offer useful features, but they also fall short in creating a modern, scalable data lake. EFS provides file-based storage accessible from multiple instances, and Redshift supports structured data analytics for large datasets. Despite their strengths, neither service provides the combination of flexible storage, ad-hoc querying, automated ETL, and integrated machine learning that modern organizations require. Redshift, for example, requires careful cluster sizing and is primarily optimized for structured data queries, limiting its adaptability for diverse and semi-structured datasets.
A comprehensive data lake architecture in AWS begins with Amazon S3, which provides highly durable, scalable, and cost-effective storage for both raw and processed datasets. By centralizing data in S3, organizations can maintain a single source of truth and store data of any type, from structured records to unstructured logs and multimedia files. This storage flexibility allows downstream services to access, analyze, and transform data efficiently without duplication or fragmentation.
AWS Glue complements S3 by providing a fully managed ETL service. Glue automates data discovery, schema management, and transformation workflows, enabling organizations to prepare raw data for analysis or machine learning tasks without extensive manual intervention. The Glue Data Catalog also serves as a centralized metadata repository, improving data governance and discoverability across the organization.
For analytics, Amazon Athena allows users to run serverless SQL queries directly against data stored in S3. This eliminates the need to provision or manage servers while providing the ability to perform ad-hoc querying and analysis on massive datasets. Athena integrates seamlessly with Glue, ensuring that data schemas are up-to-date and queries are optimized for performance.
Machine learning workloads are supported by Amazon SageMaker, which enables model building, training, and deployment at scale. SageMaker integrates with data stored in S3 and cataloged through Glue, allowing seamless transitions from raw data to predictive modeling. Organizations can leverage SageMaker for real-time, batch, or custom ML workflows, supporting a wide range of intelligent analytics applications.
Together, S3, Glue, Athena, and SageMaker form a robust, flexible, and scalable data lake platform. This combination enables organizations to perform ETL, ad-hoc querying, and advanced machine learning on diverse datasets, supporting both batch and real-time analytics. By integrating storage, transformation, analysis, and ML capabilities into a single ecosystem, AWS provides a modern solution for organizations seeking to extract actionable insights from their data efficiently and securely.