Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 3 Q31-45
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Question 31
A company wants to implement a highly available Redis cache for its web application to reduce database load. Which Google Cloud service should be used?
A) Memorystore for Redis
B) Cloud SQL
C) Firestore
D) Cloud Spanner
Answer: A
Explanation:
A high-performance caching layer is essential to reduce load on primary databases and improve response times for web applications. Memorystore for Redis provides a fully managed, in-memory key-value data store. It supports both standard and high-availability configurations. In high-availability mode, instances are automatically replicated across zones within a region, and automatic failover ensures minimal downtime if a node becomes unavailable. Memorystore handles patching, monitoring, and scaling, reducing operational overhead while maintaining consistent low latency for frequently accessed data. By using Redis as a caching layer, applications can store session data, frequently accessed objects, and computed results, which improves performance and reduces read/write operations on backend databases.
Cloud SQL is a managed relational database service designed for persistent structured data, transactional workloads, and SQL queries. While Cloud SQL can serve as a primary data store, it is not optimized for in-memory caching. Using it as a cache would increase latency compared to an in-memory store, and heavy read operations could impact the database’s transactional performance. Additionally, Cloud SQL does not provide native in-memory data structures or automatic failover optimized for caching scenarios.
Firestore is a NoSQL document database designed for structured, schema-less data. It provides real-time synchronization and offline support for applications, which makes it useful for stateful applications requiring document storage. However, Firestore is not designed for high-performance caching. Its latency for frequent, small read operations is higher than in-memory caches, and it does not provide native replication and failover optimized for ephemeral cache workloads. Using Firestore for caching would introduce unnecessary overhead and fail to meet the performance requirements of low-latency access.
Cloud Spanner is a horizontally scalable relational database with strong consistency and high availability. While it is suitable for globally distributed, mission-critical databases, it is overkill for caching requirements. Spanner focuses on persistence, scalability, and transactional consistency rather than low-latency in-memory performance. Utilizing it as a cache would not achieve the desired performance benefits and would increase costs unnecessarily.
The recommended approach is to deploy Memorystore for Redis with high-availability enabled. This ensures low-latency data access, automatic failover, and minimal operational management. The service integrates seamlessly with Google Cloud applications, supporting horizontal scaling and seamless replication. By offloading frequent queries and session data to the cache, primary databases experience reduced load, and the overall application performance improves. Memorystore provides metrics, monitoring, and alerting for observability. By leveraging its managed service features, developers can focus on application logic while maintaining high reliability, low latency, and cost efficiency. The solution meets requirements for a fast, scalable, and resilient caching layer, ensuring improved performance, durability, and operational simplicity for mission-critical applications.
Question 32
A team wants to encrypt sensitive data at rest in Cloud Storage and manage their own encryption keys. Which solution should be used?
A) Customer-Managed Encryption Keys (CMEK) with Cloud KMS
B) Default Google-managed encryption
C) Client-side encryption only
D) Cloud SQL encryption
Answer: A
Explanation:
Protecting sensitive data at rest requires both strong encryption and control over key management. Customer-Managed Encryption Keys (CMEK) allow organizations to use keys stored in Cloud Key Management Service to encrypt objects in Cloud Storage. With CMEK, the company retains full control over key rotation, usage, and revocation, enabling compliance with security policies and regulatory requirements. The integration with Cloud Storage is seamless; every object written to a bucket configured with CMEK is encrypted with the customer-managed key. This provides both security and auditability because access to the key can be logged, monitored, and controlled independently of storage access. Key rotation can be automated, and if a key is disabled or deleted, associated data becomes inaccessible, enforcing strict security controls.
Default Google-managed encryption encrypts all objects at rest automatically without requiring user action. While secure and transparent, it does not allow organizations to manage or rotate keys themselves. Relying solely on Google-managed keys means the organization has no direct control over key lifecycle, limiting the ability to meet certain compliance or internal security policies. It also does not provide granular auditing of key usage from an organizational perspective.
Client-side encryption involves encrypting objects before uploading them to Cloud Storage. While this method provides full control over encryption, key management, and security, it introduces operational complexity. Developers must implement encryption and decryption logic, manage key storage securely, and handle potential performance impacts. It also complicates access control and integration with other Google Cloud services that expect standard Cloud Storage encryption models.
Cloud SQL encryption applies to relational database instances, automatically encrypting stored data and backups. This is effective for structured data within a database but does not apply to Cloud Storage objects or unstructured data such as media, logs, or backups. Using Cloud SQL encryption would not protect data in Cloud Storage and fails to meet the requirement for storage-level encryption with user-managed keys.
The correct solution is to use Customer-Managed Encryption Keys (CMEK) with Cloud KMS. CMEK provides control, auditability, and compliance capabilities, while seamlessly encrypting objects in Cloud Storage. It ensures that sensitive data is protected according to organizational policies, allows automated key rotation, and supports integration with identity and access management. By using CMEK, organizations retain key ownership, enforce encryption policies, and maintain regulatory compliance without complicating operational workflows. This approach ensures secure, manageable, and auditable encryption for sensitive data stored in Cloud Storage, meeting requirements for both data protection and operational efficiency.
Question 33
A developer wants to deploy a stateless web application that automatically scales based on incoming HTTP requests and requires minimal operational management. Which platform should be used?
A) App Engine Standard Environment
B) Compute Engine with load balancer
C) Kubernetes Engine with manual scaling
D) Cloud SQL
Answer: A
Explanation:
A stateless web application benefits from a platform that handles scaling, patching, and infrastructure management automatically. App Engine Standard Environment is a fully managed platform that allows developers to deploy applications without managing servers or clusters. It automatically scales the application based on incoming HTTP requests, provisioning instances as demand fluctuates and terminating them during idle periods to reduce costs. This enables a pay-for-what-you-use model and ensures the application remains responsive under varying load. The environment supports multiple runtimes, handles patching and updates transparently, and integrates with logging, monitoring, and identity services. Developers can focus on writing code rather than managing infrastructure, making operational overhead minimal.
Using Compute Engine with a load balancer requires manually configuring virtual machines, scaling policies, and instance health monitoring. Although it provides full control over the environment, it significantly increases operational responsibility. Developers must monitor load, adjust instance counts, handle software updates, and manage health checks, which contrasts with the requirement for minimal operational management.
Kubernetes Engine offers container orchestration, scaling, and deployment flexibility. However, manual scaling must be explicitly configured and managed. Without automated horizontal pod autoscaling or cluster autoscaling, developers are responsible for adjusting resource allocation. Additionally, Kubernetes clusters require ongoing maintenance, patching, and monitoring, increasing operational overhead compared to a fully managed platform.
Cloud SQL is a relational database service, not a web application hosting platform. While it supports structured data storage and transactional workloads, it does not provide the runtime environment necessary to host stateless web applications. It cannot scale automatically based on HTTP request load and is therefore not suitable for application deployment.
The appropriate solution is to use App Engine Standard Environment, which provides automatic scaling, built-in monitoring, integrated logging, and fully managed infrastructure. It allows stateless applications to respond to fluctuating workloads without manual intervention, reducing operational overhead while ensuring high availability. This solution provides a managed, serverless environment optimized for web application deployment and enables developers to focus on functionality rather than infrastructure. By leveraging automatic scaling, App Engine ensures responsive and cost-efficient operation, making it ideal for stateless web applications with minimal operational management requirements.
Question 34
A company wants to restrict access to a Cloud Storage bucket so that only requests originating from its VPC network are allowed. Which configuration should be implemented?
A) VPC Service Controls perimeter with access policies
B) Public bucket with IAM roles
C) Cloud SQL firewall rules
D) Cloud CDN signed URLs
Answer: A
Explanation:
Controlling access to sensitive resources based on network origin requires a solution that enforces security boundaries. VPC Service Controls provide a mechanism to define a service perimeter around Google Cloud resources, including Cloud Storage. By creating a perimeter, administrators can restrict access to resources so that only requests originating from specific networks or trusted identity sources are allowed. This prevents data exfiltration and ensures that even authorized users cannot access the bucket from untrusted networks. The configuration integrates with identity and access management, allowing policies to be applied in conjunction with IAM roles, ensuring that users also meet identity requirements in addition to network constraints.
A public bucket with IAM roles does not restrict access based on network location. While IAM roles can control which users or service accounts can access resources, they cannot enforce that requests must originate from specific networks. Public access increases the potential attack surface and does not satisfy the requirement to allow access only from the company’s VPC network.
Firewall rules in Cloud SQL are designed to control access to SQL instances based on IP addresses. While firewall rules are effective for database instances, they do not apply to Cloud Storage objects. Using Cloud SQL firewall rules for this purpose is not technically feasible, as storage access requires network-aware policies that are enforced at the service perimeter level rather than at the database instance level.
Cloud CDN signed URLs control access to content delivered from edge caches, allowing time-limited access to specific users. While useful for controlling web content access, signed URLs are designed for content delivery purposes and do not restrict access based on VPC network origin. They also do not enforce identity-based permissions on the storage resource itself, making them insufficient for enforcing network-based access control.
The recommended solution is to implement VPC Service Controls with a defined service perimeter around the Cloud Storage bucket. This ensures that access is only possible from trusted networks, prevents exfiltration of sensitive data, and integrates with identity management for additional security enforcement. By leveraging this approach, organizations gain granular control over network-based access, minimize the risk of unauthorized access, and enforce compliance requirements for data protection. The service perimeter acts as a network-level boundary that complements IAM policies and enhances overall security. This approach ensures that only applications and users within the specified VPC can interact with the bucket, meeting the operational and security goals effectively. VPC Service Controls provide logging, auditing, and alerting capabilities, offering observability and policy enforcement that aligns with enterprise security best practices. By combining network restrictions and identity policies, organizations can enforce a robust, layered security model for Cloud Storage.
Question 35
A development team wants to implement CI/CD pipelines for multiple projects in Google Cloud that automatically build, test, and deploy containerized applications. Which service should be used?
A) Cloud Build
B) Cloud Functions
C) App Engine Standard Environment
D) Cloud SQL
Answer: A
Explanation:
Continuous integration and continuous deployment require a service capable of automating build, test, and deployment workflows for code changes. Cloud Build is a fully managed service that allows developers to define pipelines using YAML or JSON configuration files. These pipelines can automate tasks such as compiling source code, running unit and integration tests, building container images, and deploying to Kubernetes Engine, App Engine, or Cloud Run. Cloud Build integrates with source repositories, triggers builds on commits or pull requests, and supports multiple projects, enabling consistent deployment across teams. It also provides visibility into build logs, status notifications, and integration with secret management for secure access to credentials, supporting secure, automated pipelines.
Cloud Functions is a serverless compute platform designed for event-driven workloads. While it can run code automatically in response to triggers, it is not intended for building, testing, and deploying applications across multiple projects. It lacks the pipeline orchestration, testing integration, and container build features required for CI/CD workflows.
App Engine Standard Environment is a managed platform for deploying web applications. While it supports deployment of applications, it does not provide a native mechanism for orchestrating CI/CD pipelines across multiple projects, running automated tests, or building container images. Deploying directly to App Engine without a CI/CD orchestration service does not satisfy the requirement for automated build and test workflows.
Cloud SQL is a relational database service for structured data storage. It does not provide capabilities for building, testing, or deploying applications and cannot serve as a CI/CD orchestration tool. Using Cloud SQL would not address the requirements for automating pipelines or container management.
The correct solution is to use Cloud Build to orchestrate CI/CD pipelines for containerized applications. It provides automated triggers for builds, integrates with source repositories, manages artifacts, and supports deployment to various Google Cloud environments. Cloud Build ensures consistent, repeatable builds, reduces human error, and enforces quality through automated tests. By leveraging a fully managed CI/CD platform, teams can maintain reliable workflows, reduce operational overhead, and accelerate development cycles. Cloud Build also supports multi-project configurations, allowing pipelines to be centralized while maintaining project isolation. It provides observability, security integration, and granular control over build environments, making it suitable for enterprise-scale, automated software delivery. Overall, Cloud Build enables fast, repeatable, and secure deployment processes, meeting the development team’s requirements for CI/CD pipelines across multiple projects.
Question 36
A company wants to deploy a containerized application that requires secure, private communication with a Cloud SQL database without exposing the database to the public internet. Which solution should be used?
A) Private IP for Cloud SQL and VPC peering or serverless VPC access
B) Assign a public IP and restrict access with firewall rules
C) Use Cloud Functions only without networking configuration
D) Deploy database on Compute Engine VM in a separate VPC
Answer: A
Explanation:
Secure communication between cloud resources without public exposure requires private networking. Cloud SQL can be configured with a private IP, which allows it to reside within the same VPC network as the application or connect via peered networks. For serverless workloads such as Cloud Run or Cloud Functions, Serverless VPC Access connectors enable these services to communicate securely with private IP addresses in the VPC. This setup ensures that the database is never exposed to the public internet, eliminating potential attack vectors, and maintains encrypted, internal communication between services. Private IP configuration also allows integration with firewall rules and IAM policies for granular access control, enhancing overall security posture while maintaining simplicity and operational efficiency.
Assigning a public IP and restricting access with firewall rules provides some protection, but the database remains reachable over the public internet. This exposes the system to potential attack attempts, misconfigurations, and credential compromises. While firewall rules can reduce exposure, relying on public IPs introduces inherent risk and does not fully comply with best practices for private network communication.
Using Cloud Functions alone without configuring private networking does not provide secure, internal communication to the database. Serverless functions would need a public endpoint to access the database unless a VPC connector is configured. This could expose sensitive data and credentials if public IPs are used, and it does not meet the requirement for fully private communication.
Deploying a database on a Compute Engine VM in a separate VPC adds operational complexity. The team would need to manage the database software, replication, backup, scaling, and private networking manually. While private networking is possible using VPC peering, this approach introduces administrative overhead, reduces manageability, and does not leverage managed database features such as automated backups and high availability.
The correct solution is to configure Cloud SQL with a private IP and use VPC peering or Serverless VPC Access connectors for secure communication with the application. This ensures the database is accessible only within private networks, supports encrypted internal traffic, and eliminates exposure to the public internet. It also leverages the managed nature of Cloud SQL, including automatic patching, high availability, and backups, while maintaining strict network-level security. This approach reduces operational overhead, provides enterprise-grade security, and meets the requirement for private, secure connectivity between containerized applications and databases.
Question 37
A company needs to deploy multiple microservices that must scale independently and maintain service discovery between them. Which Google Cloud solution is most suitable?
A) Kubernetes Engine with multiple Deployments and Services
B) Single Compute Engine VM running all microservices
C) App Engine Standard Environment with monolithic deployment
D) Cloud Functions with all logic in one function
Answer: A
Explanation:
Microservices architectures require a deployment platform that supports independent scaling, service discovery, and orchestration. Kubernetes Engine provides these capabilities by allowing each microservice to run in a separate container organized into Deployments, which can be scaled independently based on demand or resource utilization. Services in Kubernetes abstract access to pods and provide service discovery mechanisms, allowing microservices to communicate internally without hardcoding IP addresses. Health checks, readiness probes, and rolling updates ensure minimal downtime and automatic recovery, enabling continuous operation and resilience. Kubernetes Engine also provides networking, logging, and monitoring integration, simplifying operational management while maintaining a microservices architecture.
Running multiple microservices on a single Compute Engine VM creates a monolithic deployment. This limits independent scaling because all services share the same instance resources. Failure in one service can impact the others, and updating individual services without affecting the entire system is challenging. Operational overhead increases as the team must manage resource allocation, dependencies, and deployment sequencing manually. This approach fails to provide the independent scaling and service discovery required for microservices.
App Engine Standard Environment is designed for hosting applications with minimal operational overhead. However, deploying multiple microservices as a single application reduces flexibility. Scaling is applied to the entire application rather than individual services, and routing or service discovery between components is limited. While App Engine manages scaling and runtime, it does not provide fine-grained control needed for a microservices architecture, such as inter-service communication and independent lifecycle management.
Cloud Functions is a serverless compute platform suitable for event-driven workloads. Deploying all microservice logic in a single function creates a tightly coupled design, reducing the benefits of microservices. Functions scale independently, but combining multiple services into a single function limits observability, testing, and deployment flexibility. Service-to-service communication would require additional orchestration, increasing operational complexity. This setup does not fully leverage microservices principles.
Kubernetes Engine with multiple Deployments and Services provides a scalable, resilient, and flexible platform for microservices. Each microservice can scale independently, communicate via Kubernetes Services, and be updated without affecting others. Managed features like auto-healing, rolling updates, and integration with monitoring and logging ensure reliability and observability. The platform also supports automated scaling based on CPU, memory, or custom metrics, providing cost efficiency. Network policies can enforce security, and persistent storage integration ensures stateful workloads are supported where necessary. Kubernetes Engine is the recommended solution for deploying microservices that require independent scaling, service discovery, and high availability. By leveraging its orchestration and containerization features, teams can maintain a robust, maintainable, and scalable architecture while minimizing operational overhead.
Question 38
A development team wants to analyze large amounts of structured log data in real time. Which Google Cloud service is best suited for this purpose?
A) BigQuery
B) Cloud Storage
C) Firestore
D) Memorystore
Answer: A
Explanation:
Analyzing large volumes of structured log data in real time requires a service optimized for high-performance analytics and fast querying. BigQuery is a serverless, fully managed data warehouse that allows for fast SQL queries over massive datasets. It is optimized for analytical workloads and can process terabytes or even petabytes of data efficiently. By streaming log data into BigQuery, teams can perform near real-time analysis, generate dashboards, and run complex queries without worrying about infrastructure management. BigQuery integrates with other Google Cloud services such as Cloud Logging, Cloud Pub/Sub, and Dataflow, providing a seamless pipeline for ingesting, transforming, and analyzing logs.
Cloud Storage is designed for object storage, providing durable, scalable storage for unstructured or semi-structured data. While Cloud Storage can hold large volumes of logs, it is not optimized for real-time querying or analytics. Logs stored in Cloud Storage would require additional processing, such as loading into BigQuery or using external tools, to perform real-time analysis. This adds operational complexity and latency, making it less suitable for immediate insights from structured log data.
Firestore is a NoSQL document database that provides low-latency access for structured or semi-structured data. It is excellent for operational workloads, real-time synchronization, and transactional applications, but it is not optimized for analytical workloads over large datasets. Querying large volumes of log data in Firestore is inefficient and would not scale cost-effectively for analysis at a terabyte scale. It also lacks advanced analytical functions and SQL-like querying capabilities required for deep insights.
Memorystore is a fully managed in-memory data store used for caching and real-time access to small datasets or frequently accessed data. While fast, it is not designed to handle large volumes of structured log data for analytical processing. Using Memorystore for log analytics would be cost-prohibitive and operationally impractical, as memory-based storage cannot efficiently store and query the vast datasets required for analysis.
BigQuery provides a managed, scalable, and efficient solution for analyzing structured log data. With streaming inserts, the platform supports near real-time ingestion and analysis. Teams can perform complex aggregations, filtering, and joins without managing clusters or scaling infrastructure manually. BigQuery’s serverless nature ensures that performance scales automatically with the volume of data and query complexity, and costs are controlled by query usage. Integration with Cloud Logging and Pub/Sub allows pipelines to capture logs continuously and feed them into BigQuery for actionable insights. By leveraging BigQuery, organizations gain the ability to explore, visualize, and analyze log data in real time, providing operational intelligence, monitoring, and auditing capabilities at scale. It meets requirements for performance, scalability, and ease of management, making it the ideal solution for structured log analytics.
Question 39
A team wants to deploy a containerized application that needs versioned deployments and traffic splitting for canary releases. Which Google Cloud service is most appropriate?
A) Cloud Run
B) Compute Engine
C) App Engine Flexible Environment
D) Cloud Functions
Answer: A
Explanation:
Containerized applications requiring versioned deployments and traffic splitting benefit from a serverless platform with built-in deployment management. Cloud Run allows developers to deploy multiple revisions of a container and control how traffic is distributed between versions. This enables canary releases, A/B testing, and gradual rollouts. Cloud Run automatically scales container instances based on incoming HTTP requests and integrates with Cloud Monitoring and Cloud Logging for observability. The platform provides managed networking, load balancing, and secure service-to-service communication, removing operational overhead associated with managing infrastructure. Traffic splitting allows precise control over percentages of requests sent to each version, enabling controlled testing of new features while minimizing risk to production traffic.
Compute Engine provides full control over virtual machines but does not natively support container revisions or traffic splitting. To achieve versioned deployments and canary releases, developers would need to manage multiple VMs, load balancers, and scaling policies manually. This approach increases operational complexity and requires ongoing monitoring, patching, and configuration management, making it less suitable for fast and automated canary releases.
App Engine Flexible Environment supports containerized applications and automatic scaling. While it allows versioned deployments and some traffic splitting, it is designed primarily for long-running applications and may not scale as efficiently for short-lived or stateless microservices. Configuration and deployment pipelines are more complex compared to Cloud Run, which is optimized for serverless container workloads.
Cloud Functions is a serverless platform for event-driven code. It does not support multiple container revisions, traffic splitting, or built-in canary deployment mechanisms. Deploying complex, versioned container applications with controlled traffic flow is not feasible with a single function, making it unsuitable for the requirements.
Cloud Run provides a fully managed serverless container platform that supports versioned deployments, traffic splitting, automatic scaling, and simplified operational management. By using Cloud Run, teams can implement canary releases, experiment with new features safely, and ensure high availability while avoiding infrastructure management. Integration with monitoring, logging, and IAM provides observability, security, and operational control. The platform is ideal for stateless microservices, web applications, and API endpoints requiring precise traffic control, automated scaling, and streamlined deployments. Using Cloud Run meets requirements for modern deployment strategies, reduces risk, and simplifies operational management while supporting containerized workloads efficiently.
Question 40
A company wants to collect and analyze metrics from multiple Google Cloud services to detect performance issues and trigger alerts automatically. Which service should be used?
A) Cloud Monitoring
B) Cloud Logging
C) Cloud Storage
D) BigQuery
Answer: A
Explanation:
Monitoring and alerting across multiple cloud services requires a platform that can collect metrics, visualize them, and trigger notifications based on defined thresholds. Cloud Monitoring is a fully managed service that provides observability into Google Cloud resources, including Compute Engine, Kubernetes Engine, Cloud SQL, Cloud Storage, and other services. It collects metrics such as CPU usage, memory utilization, request latency, and throughput, and allows users to create custom dashboards for visualization. Alerts can be configured to trigger notifications through email, SMS, or integrations with incident management platforms when metrics exceed specified thresholds. Cloud Monitoring also supports uptime checks, SLO/SLA tracking, and anomaly detection, enabling proactive management of system performance and rapid response to potential issues.
Cloud Logging collects and stores log entries from Google Cloud services. While logs can provide insights into errors, failures, and application behavior, they are not optimized for metric aggregation or real-time alerting on performance trends. Cloud Logging can feed into Cloud Monitoring for analysis, but by itself, it does not provide visualization, metric aggregation, or automated alerting based on thresholds.
Cloud Storage is used for durable object storage of files, backups, and static data. It does not collect or process metrics or provide alerting mechanisms. While logs or metrics can be exported to Cloud Storage for archival or analysis, they cannot automatically detect performance issues or trigger alerts.
BigQuery is a data warehouse optimized for analytics on structured and semi-structured data. While it can be used for long-term trend analysis of operational data, it is not designed for real-time monitoring or alerting. Querying BigQuery for metrics requires batch ingestion and is not suitable for triggering immediate alerts on system performance.
Cloud Monitoring provides a comprehensive solution for metric collection, visualization, and alerting across multiple services. By integrating with logs and metrics from various sources, it offers a unified view of the system’s health and enables proactive incident response. Users can define thresholds, create dashboards, and implement automated notifications to ensure that operational issues are detected and addressed promptly. Integration with alerting policies, uptime checks, and incident management tools allows teams to maintain service reliability and operational efficiency. Cloud Monitoring is specifically designed to support performance monitoring, observability, and alerting in Google Cloud, making it the most appropriate solution for collecting metrics, analyzing performance, and triggering automated alerts across multiple cloud services.
Question 41
A team wants to deploy a highly available MySQL database that automatically fails over to another zone in case of a primary instance failure. Which Google Cloud service configuration should be used?
A) Cloud SQL with high availability enabled
B) Cloud Spanner
C) Compute Engine with a manually configured MySQL cluster
D) Firestore in Native mode
Answer: A
Explanation:
High availability for relational databases requires automated failover, replication, and minimal operational overhead. Cloud SQL provides managed relational databases and allows users to enable high availability. When high availability is configured, Cloud SQL creates a primary instance and a standby instance in a different zone within the same region. In the event of a primary instance failure, the standby automatically assumes the primary role with minimal downtime, ensuring continuity of service. Backups, patching, monitoring, and failover mechanisms are handled by the service, reducing operational burden. High availability also integrates with other Google Cloud features such as private IP connectivity, automated maintenance windows, and read replicas for load distribution.
Cloud Spanner provides global, strongly consistent relational databases with high availability across regions. While Spanner is suitable for mission-critical globally distributed workloads, it is overkill for a single-region MySQL database requirement. Spanner also requires schema design and application adaptation, making it less practical for teams looking to deploy MySQL workloads with minimal migration effort.
Manually configuring a MySQL cluster on Compute Engine provides flexibility but increases operational complexity. Teams must manage replication, failover scripts, backups, patching, and monitoring themselves. Any misconfiguration could lead to downtime or data loss. This approach does not provide the automated failover and managed operational support that Cloud SQL High Availability offers, making it less suitable for teams seeking minimal management overhead.
Firestore in Native mode is a NoSQL document database that provides high availability and scalability. While it can store structured or semi-structured data, it does not support relational SQL queries, transactions in the same way as MySQL, or direct MySQL workloads. Using Firestore would require re-architecting applications and would not meet requirements for MySQL-specific high availability or automatic failover.
Enabling high availability in Cloud SQL is the recommended approach for deploying a MySQL database that requires automated failover. This configuration ensures continuity, durability, and compliance with operational best practices. The standby instance guarantees minimal downtime, and the platform manages patching, backups, and monitoring automatically. Cloud SQL’s high availability configuration also integrates with other cloud resources for secure network access, scaling, and replication management. Teams benefit from reduced operational complexity while ensuring reliable, fault-tolerant relational database services. By leveraging Cloud SQL with high availability, organizations achieve both technical reliability and operational simplicity, meeting business requirements for critical database workloads without additional manual effort or infrastructure management. This approach ensures resilience, continuity, and optimized management of relational databases.
Question 42
A company needs to store sensitive files in Cloud Storage and ensure that only specific users can download them, while allowing public write access for uploads. Which configuration meets these requirements?
A) Use IAM roles for download access and signed URLs for public uploads
B) Make the bucket fully public
C) Use Cloud SQL for storing files
D) Use Cloud CDN signed URLs only
Answer: A
Explanation:
Controlling access to sensitive files requires a combination of identity-based permissions and temporary access mechanisms. By applying IAM roles, the organization can restrict download access to specific users or service accounts. Only authorized users with the appropriate role can read objects from the bucket, ensuring that sensitive content is protected. Public write access for uploads can be achieved using signed URLs, which allow unauthenticated users to upload objects securely without exposing download access. Signed URLs have time-limited validity, ensuring that uploads are temporary and controlled. This combination allows separation of read and write permissions, protecting sensitive data while enabling open uploads where required.
Making the bucket fully public grants both read and write access to anyone on the internet. While this allows uploads, it exposes sensitive files to unauthorized downloads, violating security requirements. Full public access does not provide control over who can access stored data and introduces significant risk for data leakage.
Cloud SQL is a relational database service and is not optimized for storing large files. While binary data can be stored as BLOBs, managing public uploads and restricted downloads requires additional application logic. Cloud SQL also lacks native mechanisms for temporary public access or signed URL management, increasing complexity for handling file storage securely.
Cloud CDN signed URLs provide secure, temporary access to content distributed from edge caches. While useful for controlling downloads of cached content, Cloud CDN does not handle direct object storage uploads to Cloud Storage. It also cannot enforce IAM-based identity restrictions for downloads. Using signed URLs alone does not provide the granular control needed for restricted downloads with public uploads.
The recommended approach is to combine IAM roles for download access with signed URLs for public uploads. IAM ensures that only authorized users can read sensitive content, while signed URLs allow controlled uploads by unauthenticated users. This setup supports compliance, minimizes risk, and allows operational flexibility for file storage and distribution. By leveraging managed permissions and temporary access mechanisms, organizations can securely store sensitive files, control downloads, and provide safe upload capabilities without exposing critical data to unauthorized users. It balances security, usability, and operational efficiency while meeting the requirements for controlled access to sensitive Cloud Storage content.
Question 43
A company wants to deploy a containerized application that must run continuously and be highly available, with automatic scaling based on CPU and memory usage. Which Google Cloud service should be used?
A) Kubernetes Engine
B) Cloud Functions
C) App Engine Standard Environment
D) Cloud Storage
Answer: A
Explanation:
For running containerized applications continuously with high availability and automatic scaling based on resource utilization, Kubernetes Engine is the most suitable solution. Kubernetes Engine orchestrates containers, allowing multiple instances (pods) of a service to run simultaneously across a cluster of nodes. It provides automated load balancing, health checks, rolling updates, and self-healing capabilities. Horizontal pod autoscaling ensures that pods scale automatically based on metrics like CPU and memory usage. Cluster autoscaling adds or removes nodes depending on the overall workload, optimizing resource utilization and cost efficiency. Kubernetes Engine supports persistent storage, networking, security policies, logging, and monitoring integrations, ensuring a robust production-ready environment for containerized applications. By running containers in a managed cluster, teams can focus on application logic rather than infrastructure management.
Cloud Functions is designed for event-driven serverless workloads and is not intended to run continuously. Functions scale automatically based on events but cannot provide continuous execution for long-running services. There is also no concept of pods, persistent state, or node-level autoscaling, making Cloud Functions unsuitable for applications that require continuous operation and fine-grained scaling control.
App Engine Standard Environment allows automatic scaling and is suitable for stateless applications. However, it does not provide container-level orchestration or control over scaling based on CPU and memory utilization. Deployment is typically limited to application runtimes provided by Google, and the environment is less flexible for running complex containerized workloads requiring multi-container setups, network policies, or persistent volumes.
Cloud Storage is an object storage service for unstructured data. While it can store files, backups, and static content, it does not provide compute capabilities or container orchestration. It is incapable of running applications or managing service availability, scaling, or health checks. Using Cloud Storage alone does not meet the requirements for deploying a continuously running, scalable application.
Kubernetes Engine is the correct choice because it provides a fully managed platform for orchestrating containerized applications. It ensures high availability through multi-zone deployments and node redundancy. With horizontal pod autoscaling and cluster autoscaling, workloads are automatically adjusted based on CPU, memory, or custom metrics. Built-in monitoring, logging, and alerting integrate with Cloud Monitoring, providing observability and operational control. Security policies, role-based access, and networking configurations protect the application and data. Rolling updates allow zero-downtime deployments, ensuring reliable service continuity. Kubernetes Engine is suitable for both stateless and stateful workloads, with support for persistent storage through PersistentVolumes and StatefulSets. It enables teams to maintain a resilient, scalable, and manageable infrastructure while focusing on business logic and feature development. This approach satisfies the requirement for continuous operation, high availability, and automatic scaling based on resource utilization, providing an enterprise-grade platform for containerized workloads.
Question 44
A development team wants to automate data processing tasks triggered by file uploads to Cloud Storage and scale automatically based on workload. Which Google Cloud service should be used?
A) Cloud Functions
B) Compute Engine
C) Kubernetes Engine
D) Cloud SQL
Answer: A
Explanation:
Automating data processing tasks that respond to events requires an event-driven, serverless platform that scales based on workload. Cloud Functions is designed precisely for this scenario. Developers can configure a function to trigger automatically whenever a file is uploaded to a Cloud Storage bucket. The function executes in a fully managed, serverless environment, scaling automatically to handle multiple concurrent events without requiring manual infrastructure management. Billing is based on actual execution time and resources used, providing cost efficiency. Cloud Functions integrates with other Google Cloud services, such as Pub/Sub, BigQuery, and Cloud Logging, enabling complex workflows and data pipelines. This allows teams to process data immediately after upload, generate alerts, or perform transformations, all while maintaining minimal operational overhead.
Compute Engine provides virtual machines that can run any workload but requires manual management of scaling, health checks, and triggers. Automating file-based processing would require writing scripts to poll storage buckets, configuring load balancers, and managing VM instances, introducing operational complexity and latency. Compute Engine does not natively support event-driven execution or serverless scaling, making it less suitable for automated data processing triggered by file uploads.
Kubernetes Engine provides orchestration for containers and supports workload scaling, but it requires additional configuration for event-driven triggers. Implementing automatic processing of file uploads would require building custom controllers or integrating with Pub/Sub, increasing operational complexity. While Kubernetes Engine can handle high-volume workloads, it is not inherently serverless and requires infrastructure management, which contrasts with the requirement for minimal operational overhead.
Cloud SQL is a relational database service that stores structured data. It does not provide event-driven compute capabilities and cannot process file uploads or execute functions. Using Cloud SQL for this purpose would require additional compute resources to implement triggers and processing, making it inefficient and impractical for automated workflows.
Cloud Functions is the correct solution because it provides serverless, event-driven execution with automatic scaling. Developers can focus entirely on the processing logic rather than infrastructure management. It supports integration with storage triggers, Pub/Sub topics, logging, and monitoring, allowing reliable, scalable, and maintainable workflows. Cloud Functions provides near real-time execution for uploaded files, enabling immediate data processing without manual intervention. It ensures consistent, scalable handling of workload spikes while maintaining low operational costs. With its serverless architecture, developers can implement robust, event-driven data pipelines that respond automatically to file uploads, process data efficiently, and scale seamlessly as workload volume fluctuates. Cloud Functions is fully aligned with the requirements for automation, scalability, and minimal management overhead in cloud-based data processing scenarios.
Question 45
A company wants to provide temporary, time-limited access to private Cloud Storage objects for external partners. Which solution should be used?
A) Signed URLs
B) Public bucket access
C) Cloud Functions triggers
D) Cloud SQL IAM authentication
Answer: A
Explanation:
Providing temporary, time-limited access to private Cloud Storage objects requires a mechanism that allows secure access without exposing the entire bucket or requiring permanent credentials. Signed URLs provide this functionality. A signed URL is a URL that includes an expiration time and a signature generated using a service account key. The external partner can use the URL to download or upload an object for a limited period, after which the URL expires automatically. This method ensures secure, controlled access without exposing IAM credentials or making the objects public. Signed URLs can be generated programmatically and integrated into workflows or applications, providing flexible and temporary access for partners or external users.
Public bucket access exposes all objects in the bucket to anyone on the internet. While it allows downloads, it does not provide time-limited control or security. Any user can access all objects without restriction, making it inappropriate for scenarios requiring temporary or selective access. Public buckets increase the risk of unauthorized data exposure, violating security requirements.
Cloud Functions triggers can execute code in response to events, but do not provide a mechanism for granting time-limited access to storage objects. Functions can create signed URLs as part of a workflow, but by themselves, they do not solve the requirement for temporary access. Using only triggers without signed URLs would not allow controlled external downloads or uploads.
Cloud SQL IAM authentication allows database clients to authenticate without passwords using IAM credentials. It is specific to Cloud SQL instances and does not provide access management for Cloud Storage objects. Using Cloud SQL authentication for storage access is not feasible and does not meet the requirement for temporary access to objects.
Signed URLs are the recommended solution because they provide secure, temporary access to Cloud Storage objects. They allow external partners to interact with specific files without requiring permanent credentials or public access. Expiration times ensure that access is automatically revoked, and permissions can be tightly controlled. Signed URLs integrate with applications or workflows and support both upload and download operations. By generating URLs programmatically, organizations maintain control over sensitive data while facilitating external collaboration. This approach balances security, convenience, and operational simplicity, ensuring that sensitive storage objects are accessible only to intended users for a limited period. Signed URLs are widely used for secure content delivery, temporary sharing, and partner integration, making them the ideal solution for controlled, time-limited access to private Cloud Storage resources.