Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 3 Q 41- 60

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Question 41

You need to deploy a web application that automatically scales based on HTTP traffic and supports multiple microservices communicating with each other. Which Google Cloud service combination is most appropriate?

A) Compute Engine with load balancer
B) App Engine Standard Environment
C) Kubernetes Engine (GKE) with Istio service mesh
D) Cloud Run with Cloud SQL

Answer C) Kubernetes Engine (GKE) with Istio service mesh

Explanation

A) Compute Engine with a load balancer allows you to deploy VM instances to serve web traffic and scale horizontally with managed instance groups. While it provides high availability and scaling capabilities, it does not natively manage containers or microservices. Teams must manually deploy and orchestrate containers, handle service-to-service communication, manage rolling updates, and implement retries or circuit breakers for microservices. For complex microservice architectures, using Compute Engine alone adds operational overhead and reduces agility.

B) App Engine Standard Environment is a fully managed serverless platform that automatically scales based on traffic. It supports microservices to some extent but is limited to specific runtimes and lacks fine-grained control over service-to-service communication. Features like advanced traffic splitting, retries, and observability are constrained compared to Kubernetes solutions. Additionally, deploying multiple microservices with interdependencies in App Engine requires careful configuration and may not be as flexible as a container orchestration platform.

C) Kubernetes Engine (GKE) is a fully managed container orchestration platform that provides automatic scaling, rolling updates, multi-zone high availability, and orchestration for complex microservice architectures. Adding Istio, a service mesh, enhances the architecture by providing secure service-to-service communication, traffic management, observability, retries, circuit breaking, and policy enforcement. Together, GKE and Istio support scalable, resilient, and secure microservices with automated scaling based on HTTP requests or custom metrics. This combination simplifies deployment, reduces downtime during updates, and provides advanced features such as telemetry and security policies for microservices, making it ideal for modern cloud-native applications.

D) Cloud Run is a serverless container platform that automatically scales based on HTTP requests. While it supports stateless microservices, it lacks advanced orchestration, service-to-service communication management, and network policies available in GKE with Istio. Cloud Run is suitable for simple or lightweight microservices but may not provide the necessary control, observability, or resilience for complex applications requiring multiple interdependent services.

The correct choice is Kubernetes Engine with Istio because it provides complete container orchestration, advanced microservice management, automatic scaling, secure service-to-service communication, and observability. This architecture supports complex cloud-native applications, simplifies deployment, reduces operational risk, and ensures high availability while enabling teams to implement modern DevOps practices such as CI/CD, blue-green deployments, and canary releases.

Question 42

You need to create a pipeline that ingests large volumes of log data from multiple sources in near real time, performs transformations, and stores the data in BigQuery for analytics. Which combination of services is most appropriate?

A) Cloud Functions and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery
C) Cloud Run and Cloud SQL
D) Compute Engine and Cloud Bigtable

Answer B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery

Explanation

A) Cloud Functions and Cloud Storage can handle event-driven workflows where individual files are uploaded and processed. While serverless and simple to implement, this approach does not scale efficiently for high-throughput log ingestion or near real-time processing. Cloud Functions cannot efficiently handle large-scale streaming data, batching, windowing, or complex transformations without creating multiple interdependent functions, increasing operational complexity.

B) Cloud Pub/Sub is a messaging service that reliably ingests high-throughput streaming data from multiple sources. It provides at-least-once delivery, buffering, and horizontal scaling. Cloud Dataflow is a fully managed data processing platform for batch and stream processing that supports complex transformations, filtering, aggregations, and windowed computations using Apache Beam. BigQuery is a fully managed, serverless data warehouse optimized for analytics. This combination provides a cloud-native, scalable, and near real-time analytics pipeline that ingests, transforms, and stores log data efficiently. The pipeline supports variable load, fault tolerance, and operational observability, enabling teams to generate timely insights and maintain high reliability.

C) Cloud Run and Cloud SQL provide a containerized serverless environment with relational database storage. While Cloud Run scales automatically, Cloud SQL is not designed for high-throughput streaming analytics. Using this combination for real-time ingestion and transformation would lead to performance bottlenecks, operational overhead, and difficulty handling large volumes of logs efficiently.

D) Compute Engine and Cloud Bigtable provide flexibility for VM-based workloads and NoSQL storage. While Cloud Bigtable supports high-throughput ingestion, Compute Engine requires manual orchestration of streaming workloads and scaling, and Bigtable does not offer the analytical querying capabilities of BigQuery. Therefore, this combination is not optimal for real-time analytics pipelines requiring complex transformations and reporting.

The correct solution is Cloud Pub/Sub, Cloud Dataflow, and BigQuery because this combination provides a fully managed, scalable, and serverless architecture for near real-time log analytics. Pub/Sub handles ingestion, Dataflow performs transformations, and BigQuery stores and queries data efficiently. This architecture reduces operational overhead, ensures reliability, supports analytics at scale, and provides observability and monitoring capabilities for production pipelines.

Question 43

You need to provide temporary, secure access to a set of Cloud Storage objects for external collaborators without creating additional IAM accounts. Which solution is most appropriate?

A) Share your personal credentials
B) Use a service account with long-lived keys
C) Generate signed URLs
D) Grant Owner permissions to all external users

Answer C) Generate signed URLs

Explanation

A) Sharing personal credentials is highly insecure and violates the principle of least privilege. Anyone with your credentials can access all resources in the project, potentially causing unauthorized changes or data breaches. It also makes auditing and revocation difficult. Sharing credentials is never recommended for temporary or limited access scenarios.

B) Using a service account with long-lived keys provides programmatic access but is not suitable for temporary, limited access. Long-lived keys require careful management, and sharing them with external collaborators introduces significant security risks. If keys are compromised, attackers can gain access to project resources beyond the intended scope.

C) Generating signed URLs provides temporary, secure access to specific Cloud Storage objects without creating IAM accounts. Signed URLs allow you to define access permissions (read or write) and expiration times. This method ensures that external collaborators can perform their tasks without exposing credentials or granting permanent access. Signed URLs are auditable, secure, and automatically expire, preventing long-term exposure. They also support fine-grained access control, enabling compliance with least-privilege principles and regulatory requirements.

D) Granting Owner permissions to external users is extremely insecure. Owner access provides full control over all resources, including the ability to modify IAM policies and delete data. This approach is excessive for temporary access and violates security best practices.

The correct approach is signed URLs because they balance security, ease of use, and operational efficiency. Signed URLs enable temporary access to specific objects without exposing credentials, support expiration times, and provide an auditable and scalable solution for sharing resources with external collaborators.

Question 44

You need to implement a monitoring solution that alerts your operations team when CPU utilization exceeds 75%, disk usage exceeds 90%, or memory usage exceeds 80% on Compute Engine instances. Which service should you use?

A) Cloud Logging
B) Cloud Monitoring with alerting policies
C) Cloud Trace
D) Cloud Storage notifications

Answer B) Cloud Monitoring with alerting policies

Explanation

A) Cloud Logging collects logs from Compute Engine and other Google Cloud services. While useful for auditing and troubleshooting, Cloud Logging does not natively monitor system-level metrics or trigger threshold-based alerts. Exporting logs to BigQuery or other tools allows analysis but does not provide real-time alerting for CPU, memory, or disk metrics.

B) Cloud Monitoring is designed for real-time collection and monitoring of system metrics, including CPU usage, memory consumption, and disk I/O. Alerting policies allow thresholds to be defined for these metrics, and notifications can be sent automatically to email, Slack, PagerDuty, or other channels when thresholds are exceeded. Cloud Monitoring supports dashboards, trend analysis, and historical metrics visualization, providing operational visibility and proactive incident response. This solution ensures that operations teams are alerted promptly, can investigate issues, and maintain high availability and performance of Compute Engine workloads.

C) Cloud Trace is a performance analysis tool that tracks latency across distributed applications. While it helps optimize application performance, it does not monitor system-level metrics such as CPU, memory, or disk usage, and it cannot trigger automated alerts for infrastructure thresholds.

D) Cloud Storage notifications alert users about changes to storage objects, such as creation or deletion. This service is unrelated to Compute Engine system metrics and cannot be used to monitor CPU, memory, or disk usage.

The correct solution is Cloud Monitoring with alerting policies. It provides real-time monitoring, automated alerts, and visualization of metrics across Compute Engine instances. This ensures proactive response to performance issues, supports capacity planning, and maintains high availability for critical applications. Cloud Monitoring integrates seamlessly with other Google Cloud services for centralized observability and operational efficiency.

Question 45

You need to implement a disaster recovery strategy for a mission-critical application that must remain available even during a regional outage. Which architecture is most appropriate?

A) Single-region deployment with automated backups
B) Multi-region deployment with active-active instances
C) Single-region deployment with snapshots
D) Deploy all resources in a private VPC

Answer B) Multi-region deployment with active-active instances

Explanation

A) Single-region deployment with automated backups ensures data recovery in the event of accidental deletion or data corruption. However, it does not provide protection against region-wide outages. If the region hosting the application fails, the application will be unavailable until it is manually restored in another region, violating near-zero downtime requirements for mission-critical systems.

B) Multi-region deployment with active-active instances provides continuous availability across multiple regions. Traffic is distributed using a global load balancer, and healthy instances in other regions handle requests automatically during a regional outage. This architecture meets strict recovery objectives (RTO and RPO), ensures minimal downtime, and provides resilience against region-wide failures. Active-active deployments also allow for load distribution, scaling, and operational continuity, aligning with cloud-native best practices for mission-critical applications.

C) Single-region deployment with snapshots allows recovery but requires manual restoration in a different region. This approach introduces significant downtime and cannot meet near-zero availability requirements. Snapshots alone do not provide automated failover or redundancy, making it unsuitable for critical applications.

D) Deploying all resources in a private VPC enhances security and network isolation but does not provide redundancy across regions. If the region fails, the application and resources become unavailable, making this solution insufficient for disaster recovery objectives.

The correct solution is multi-region deployment with active-active instances. This design ensures redundancy, automated failover, and near-zero downtime. By distributing workloads across multiple regions, implementing health checks, and using global load balancing, organizations achieve resilience, operational continuity, and high availability for mission-critical applications. This architecture aligns with enterprise disaster recovery best practices and cloud-native design principles.

Question 46

You are tasked with providing secure, temporary access to a Cloud Storage bucket for a third-party contractor to upload files. Which approach is the most secure and efficient?

A) Share your personal credentials
B) Create a service account with long-lived keys
C) Generate signed URLs
D) Grant Owner permissions to the contractor

Answer C) Generate signed URLs

Explanation

A) Sharing personal credentials is highly insecure. It violates the principle of least privilege and exposes all resources in your Google Cloud project to the contractor. This method makes auditing and revocation extremely difficult and introduces significant security risk. Personal credentials may also have elevated permissions, granting more access than necessary, which is a major concern in enterprise security practices.

B) Creating a service account with long-lived keys provides programmatic access, but it is inappropriate for temporary access. Long-lived keys require secure storage and careful rotation. Sharing them with a contractor increases the risk of accidental exposure or malicious use, which could compromise your cloud environment. The management overhead and security risks make this approach suboptimal for temporary access scenarios.

C) Generating signed URLs is the most secure and efficient method. Signed URLs allow temporary, restricted access to specific objects in a Cloud Storage bucket without sharing credentials or IAM roles. You can define the HTTP method (GET, PUT) and set an expiration time for the URL. This ensures that the contractor can perform the required upload while access automatically expires, reducing the risk of misuse. Signed URLs provide auditability, granularity, and operational simplicity, aligning perfectly with security best practices for temporary access.

D) Granting Owner permissions to the contractor is excessive and highly insecure. Owner permissions provide full control over the project and all resources, allowing deletion or modification beyond the scope of the task. This approach violates the principle of least privilege and is unsuitable for temporary access needs.

The correct choice is generating signed URLs. This approach balances security, operational simplicity, and compliance. It allows temporary access without exposing credentials, supports granular control, and can be audited and revoked automatically. Signed URLs are the recommended method for providing third-party access to Cloud Storage objects securely and efficiently.

Question 47

You need to design a solution that ingests IoT sensor data in real time, performs transformations, and makes the results available for analytics in BigQuery. Which Google Cloud architecture is best suited?

A) Cloud Functions and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery
C) Cloud Run and Cloud SQL
D) Compute Engine and Cloud Bigtable

Answer B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery

Explanation

A) Cloud Functions and Cloud Storage can handle event-driven tasks, such as processing individual files as they arrive. While serverless and simple to implement, this approach is not suitable for high-throughput real-time IoT data streams. Cloud Functions lack native support for batching, windowing, and complex transformations, making it challenging to scale efficiently or maintain near real-time analytics for large volumes of sensor data.

B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery form a robust, fully managed, serverless pipeline for real-time data analytics. Pub/Sub provides reliable, scalable ingestion of IoT events with at-least-once delivery. Dataflow allows stream processing with complex transformations, aggregations, and windowed computations, ensuring data is clean and structured before loading into BigQuery. BigQuery serves as a high-performance analytical data warehouse, supporting SQL-based querying and reporting on transformed data. This combination enables scalable, fault-tolerant ingestion, processing, and analytics with near-zero operational overhead, making it ideal for enterprise IoT solutions.

C) Cloud Run and Cloud SQL can handle stateless workloads and relational storage. While Cloud Run scales automatically, Cloud SQL is not designed for high-throughput streaming analytics. Using this combination for real-time IoT pipelines may result in performance bottlenecks and increased operational complexity, making it less suitable for enterprise-scale deployments.

D) Compute Engine and Cloud Bigtable provide flexible VM-based workloads and high-throughput NoSQL storage. While Cloud Bigtable supports large-scale ingestion, Compute Engine requires manual orchestration for streaming processing, scaling, and transformation. Additionally, Cloud Bigtable does not provide analytical querying like BigQuery, limiting its suitability for end-to-end analytics solutions.

The correct architecture is Cloud Pub/Sub, Cloud Dataflow, and BigQuery. This architecture ensures high-throughput ingestion, real-time processing, and analytics-ready storage. It supports operational scalability, monitoring, and automation while minimizing management overhead. This approach aligns with cloud-native best practices for IoT data pipelines, providing a resilient and cost-effective solution for real-time analytics.

Question 48

You need to monitor Compute Engine instances for CPU, memory, and disk usage, and send alerts to your operations team when thresholds are exceeded. Which service is most appropriate?

A) Cloud Logging
B) Cloud Monitoring with alerting policies
C) Cloud Trace
D) Cloud Storage notifications

Answer B) Cloud Monitoring with alerting policies

Explanation

A) Cloud Logging captures logs from Compute Engine and other services. While it allows historical analysis and troubleshooting, it is not designed to monitor real-time system metrics or trigger threshold-based alerts for CPU, memory, or disk usage. Using Cloud Logging for monitoring would require custom processing pipelines, adding complexity and latency.

B) Cloud Monitoring is designed to collect system-level metrics from Compute Engine, including CPU utilization, memory consumption, and disk I/O. Alerting policies can be configured to notify the operations team via email, Slack, PagerDuty, or other channels when metrics exceed predefined thresholds. Dashboards and visualization tools provide operational visibility and historical trends. Cloud Monitoring supports automated alerts, proactive operational response, and scalable infrastructure monitoring. This service ensures that the operations team can address issues promptly, maintain high availability, and plan capacity effectively.

C) Cloud Trace is used for performance monitoring and latency tracking of distributed applications. While valuable for identifying bottlenecks in application requests, it does not provide metrics for system-level monitoring such as CPU or memory utilization and cannot trigger alerts for infrastructure-level issues.

D) Cloud Storage notifications alert users about changes to storage objects, such as file creation or deletion. They are unrelated to monitoring Compute Engine metrics and cannot notify teams about CPU, memory, or disk thresholds.

The correct solution is Cloud Monitoring with alerting policies. This provides real-time monitoring, automated notifications, and visualization of metrics across Compute Engine instances. It ensures proactive incident response, maintains operational continuity, and supports enterprise observability best practices. Cloud Monitoring integrates with logging and other tools, providing a comprehensive operational monitoring solution.

Question 49

You need to provide secure access to a Cloud SQL database from multiple GKE clusters across different projects. Which approach is most secure and scalable?

A) Use root database credentials in ConfigMaps
B) Create Kubernetes Secrets for service accounts and use IAM authentication
C) Hard-code passwords in container images
D) Share database credentials with all clusters

Answer B) Create Kubernetes Secrets for service accounts and use IAM authentication

Explanation

A) Using root database credentials in ConfigMaps is insecure because ConfigMaps are not encrypted and can be accessed by any pod with access. This exposes sensitive database credentials and violates security best practices. It also complicates rotation and auditing of credentials.

B) Creating Kubernetes Secrets for service accounts and leveraging IAM authentication provides a secure and scalable solution. Secrets are stored encrypted in the Kubernetes cluster and can be mounted into pods or injected as environment variables. IAM authentication allows controlled access to Cloud SQL instances, ensuring that only authorized service accounts can connect. This approach supports secret rotation, auditing, and least-privilege access while maintaining operational efficiency across multiple clusters and projects.

C) Hard-coding passwords in container images is insecure and violates best practices. Images may be shared, inspected, or stored in registries, exposing credentials. This approach complicates rotation and increases the risk of compromise.

D) Sharing database credentials with all clusters is insecure and violates the principle of least privilege. Any compromise in one cluster could affect all clusters, increasing operational and security risks.

The correct solution is using Kubernetes Secrets with service accounts and IAM authentication. This approach ensures secure, scalable, and auditable access to Cloud SQL, aligns with cloud-native best practices, supports multiple clusters, and enables proper secret management without exposing sensitive credentials.

Question 50

You need to implement a disaster recovery plan for a mission-critical application that requires near-zero downtime during a regional outage. Which architecture is most appropriate?

A) Single-region deployment with automated backups
B) Multi-region deployment with active-active instances
C) Single-region deployment with snapshots
D) Deploy all resources in a private VPC

Answer B) Multi-region deployment with active-active instances

Explanation

A) Single-region deployment with automated backups provides data recovery from accidental deletion or corruption but does not protect against region-wide failures. If the region hosting the application goes down, recovery in another region takes time, violating near-zero downtime requirements.

B) Multi-region deployment with active-active instances ensures continuous availability across multiple regions. Traffic is distributed via a global load balancer, and healthy instances in other regions automatically handle requests if one region fails. This architecture meets strict recovery objectives (RTO and RPO), minimizes downtime, and provides resilience and operational continuity. Active-active deployments also support load balancing, scaling, and fault tolerance. This approach aligns with cloud-native disaster recovery best practices for mission-critical applications.

C) Single-region deployment with snapshots allows recovery but requires manual restoration in a different region. This introduces significant downtime and does not provide automated failover or redundancy, making it unsuitable for applications requiring near-zero downtime.

D) Deploying all resources in a private VPC enhances security but does not provide cross-region redundancy. If the region fails, the application and resources are unavailable, which is insufficient for disaster recovery objectives.

The correct architecture is multi-region deployment with active-active instances. This ensures redundancy, automated failover, and minimal downtime. By distributing workloads across regions, implementing health checks, and using global load balancing, organizations can achieve resilience, operational continuity, and high availability for mission-critical applications, aligning with best practices for enterprise disaster recovery.

Question 51

You need to deploy a containerized application that must scale automatically based on CPU utilization and provide zero-downtime updates. Which Google Cloud service is best suited?

A) Compute Engine with managed instance groups
B) App Engine Standard Environment
C) Cloud Run
D) Kubernetes Engine (GKE)

Answer D) Kubernetes Engine (GKE)

Explanation

A) Compute Engine with managed instance groups provides VM-based scaling and high availability. While it allows scaling based on CPU or load, deploying containerized applications requires installing and managing container runtimes manually. Rolling updates and zero-downtime deployments must be scripted or managed with complex orchestration, which increases operational overhead. This approach is more suitable for traditional VM-based workloads than modern containerized applications.

B) App Engine Standard Environment offers automatic scaling and serverless management. It simplifies deployment and handles scaling automatically. However, it restricts runtime environments to predefined supported languages and does not provide granular control over container orchestration. Zero-downtime deployments are limited and may require careful traffic splitting, which is less flexible for complex containerized workloads or multi-service architectures.

C) Cloud Run is a serverless container platform that automatically scales based on HTTP requests and supports stateless applications. While it provides automated scaling and can handle rolling updates, it does not support advanced orchestration for multiple interdependent containers or complex microservices. Cloud Run is excellent for microservices but may not be sufficient for enterprise-grade applications requiring complex deployment strategies and multiple container services.

D) Kubernetes Engine (GKE) is a fully managed Kubernetes service that provides advanced container orchestration, automatic scaling, rolling updates, and zero-downtime deployment strategies. Multi-zone or multi-region clusters provide high availability, and integrations with load balancing, monitoring, and IAM enable secure and efficient management. GKE supports multiple interdependent containers, service meshes, and automated CI/CD pipelines. This makes it ideal for enterprise containerized applications requiring scalable, resilient, and flexible deployments.

The correct solution is GKE because it provides complete orchestration, rolling updates, high availability, and automated scaling. It is ideal for containerized applications that need zero-downtime updates while supporting operational efficiency, resilience, and advanced deployment strategies.

Question 52

You are designing a streaming data pipeline for IoT sensor data that must handle variable load and provide analytics in BigQuery. Which combination of services is most appropriate?

A) Cloud Functions and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery
C) Cloud Run and Cloud SQL
D) Compute Engine and Cloud Bigtable

Answer B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery

Explanation

A) Cloud Functions and Cloud Storage can handle event-driven workloads triggered by file uploads or HTTP requests. While serverless and easy to implement, this approach is not suitable for high-throughput IoT streaming data. Cloud Functions lack native support for batching, windowing, and transformations required for large-scale real-time analytics. Scaling logic can become complex, and failure handling requires additional operational overhead.

B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery provide a robust serverless solution for streaming analytics. Pub/Sub ensures reliable ingestion with horizontal scaling and at-least-once delivery. Dataflow enables complex stream processing, including aggregations, filtering, and windowed computations, using Apache Beam. BigQuery stores processed data for analytics, providing fast SQL-based querying. This combination ensures near real-time ingestion, processing, and analysis, supports variable load, and minimizes operational overhead. Cloud-native scalability, monitoring, and fault tolerance make this architecture ideal for IoT analytics.

C) Cloud Run and Cloud SQL are suitable for containerized stateless workloads and relational storage. Cloud Run scales automatically but is limited for high-throughput stream processing. Cloud SQL is not designed for real-time analytics on large-scale IoT data. This combination may result in performance bottlenecks, latency issues, and operational challenges.

D) Compute Engine and Cloud Bigtable provide flexibility and high throughput but require manual orchestration of streaming, transformation, and scaling. Cloud Bigtable is a high-performance NoSQL database but does not provide analytical query capabilities like BigQuery. This approach introduces significant operational overhead and is not ideal for near real-time analytics.

The correct architecture is Cloud Pub/Sub, Cloud Dataflow, and BigQuery because it provides scalable, resilient, and fully managed stream ingestion, transformation, and storage for analytics. It supports variable workloads, reduces operational complexity, and aligns with cloud-native best practices for enterprise IoT solutions.

Question 53

You need to allow temporary access for an external contractor to upload files to a Cloud Storage bucket without creating IAM accounts. Which method is most secure?

A) Share your credentials
B) Create a service account with a long-lived key
C) Use signed URLs
D) Grant Owner permissions

Answer C) Use signed URLs

Explanation

A) Sharing credentials is insecure and violates least-privilege principles. Anyone with access can interact with all project resources, creating a major security risk. It also complicates auditing and revocation. Sharing credentials is not recommended for temporary or scoped access.

B) Creating a service account with a long-lived key provides programmatic access but is unsuitable for temporary access. Long-lived keys introduce security risks if compromised and require careful rotation and management. Sharing these keys increases the potential for misuse and violates best practices.

C) Signed URLs provide secure, temporary access to specific objects in a Cloud Storage bucket without creating IAM accounts. You can define permissions (read or write) and expiration times, ensuring the contractor can perform the required uploads while access automatically expires. Signed URLs are auditable, easy to manage, and align with least-privilege and security best practices. This method is secure, scalable, and operationally efficient.

D) Granting Owner permissions is excessive and highly insecure. Owners have full control over all project resources, which is unnecessary and dangerous for temporary file uploads. This violates security best practices.

The correct solution is signed URLs, as they provide secure, temporary, and auditable access for contractors without exposing credentials or granting permanent permissions.

Question 54

You need to monitor system metrics on Compute Engine instances and automatically alert the operations team when thresholds are exceeded. Which service should you use?

A) Cloud Logging
B) Cloud Monitoring with alerting policies
C) Cloud Trace
D) Cloud Storage notifications

Answer B) Cloud Monitoring with alerting policies

Explanation

A) Cloud Logging captures logs from Compute Engine and other services for auditing and troubleshooting. While logs can be analyzed, they do not provide real-time monitoring or threshold-based alerts for CPU, memory, or disk metrics. Using logs for monitoring would require custom pipelines and would not provide immediate alerting capabilities.

B) Cloud Monitoring collects system-level metrics including CPU utilization, memory usage, and disk I/O. Alerting policies allow thresholds to be defined, and notifications can be sent via email, Slack, or PagerDuty. Dashboards provide visualization and trend analysis for proactive management. Cloud Monitoring ensures operational teams are alerted in real time, supports capacity planning, and provides enterprise observability, making it ideal for maintaining high availability of Compute Engine workloads.

C) Cloud Trace is focused on latency and request tracing across distributed applications. It helps identify performance bottlenecks in applications but does not monitor system-level metrics or trigger infrastructure alerts.

D) Cloud Storage notifications alert users to object creation or deletion events in Cloud Storage. They are unrelated to monitoring system metrics on Compute Engine instances.

The correct solution is Cloud Monitoring with alerting policies, which provides real-time monitoring, automated alerts, visualization, and operational insights to maintain high availability and performance.

Question 55

You need to design a disaster recovery solution for a critical application that must remain available during a regional outage. Which architecture is most suitable?

A) Single-region deployment with automated backups
B) Multi-region deployment with active-active instances
C) Single-region deployment with snapshots
D) Deploy all resources in a private VPC

Answer B) Multi-region deployment with active-active instances

Explanation

A) Single-region deployment with automated backups provides data recovery in case of accidental deletion but does not protect against region-wide outages. If the region fails, downtime is unavoidable until resources are restored in another region. This violates near-zero downtime requirements.

B) Multi-region deployment with active-active instances ensures continuous availability across multiple regions. Traffic is distributed using a global load balancer, and healthy instances in other regions automatically handle requests during regional failures. This architecture meets strict recovery objectives (RTO and RPO), ensures minimal downtime, and supports operational continuity. Active-active deployments also allow for load balancing, scalability, and fault tolerance, making it ideal for mission-critical applications.

C) Single-region deployment with snapshots allows recovery but requires manual restoration in another region. This introduces downtime and does not provide automated failover or high availability. Snapshots alone are insufficient for disaster recovery of critical workloads.

D) Deploying all resources in a private VPC enhances security but does not provide cross-region redundancy. A regional failure would make all resources unavailable, which is unacceptable for disaster recovery objectives.

The correct solution is multi-region deployment with active-active instances, providing redundancy, automatic failover, and near-zero downtime. By distributing workloads across regions and using global load balancing, organizations achieve resilience, operational continuity, and high availability for mission-critical applications, aligning with enterprise disaster recovery best practices.

Question 56

You need to migrate an on-premises MySQL database to Cloud SQL with minimal downtime and continuous replication. Which approach is most appropriate?

A) Export database to SQL dump and import into Cloud SQL
B) Use Database Migration Service (DMS) for continuous replication
C) Manual schema creation and data copy
D) Cloud Storage Transfer Service

Answer B) Use Database Migration Service (DMS) for continuous replication

Explanation

A) Exporting an on-premises MySQL database to a SQL dump and importing it into Cloud SQL is suitable for small datasets or test environments. However, it introduces significant downtime because the source database must stop updates during export to ensure data consistency. Any transactions after the export will not be captured, which makes this method unsuitable for production workloads that require minimal downtime. Additionally, for large datasets, the export and import process can take a long time, further increasing downtime.

B) Database Migration Service (DMS) is a fully managed solution for migrating databases to Cloud SQL with minimal downtime. It supports continuous replication, which captures ongoing changes in real time, ensuring that the target Cloud SQL database remains synchronized with the source. DMS automates schema migration, initial data seeding, and continuous replication, allowing production applications to continue running with minimal disruption. DMS also provides monitoring, logging, and error-handling features that enhance reliability and transparency. This makes it ideal for enterprise workloads requiring high availability during migration.

C) Manual schema creation and data copy is time-consuming, error-prone, and operationally complex. Maintaining data consistency between the source and target databases requires writing custom scripts and continuous monitoring. This approach is not feasible for minimal downtime migration and carries a high risk of data loss or inconsistency.

D) Cloud Storage Transfer Service is designed for bulk file transfers between storage systems. While useful for moving files or backups, it cannot handle relational database schema, transactional integrity, or continuous replication. It is unsuitable for database migration requiring minimal downtime.

The correct approach is using Database Migration Service because it provides automation, continuous replication, monitoring, and minimal downtime. It ensures data integrity, reliability, and operational efficiency while allowing the source database to remain operational throughout the migration process.

Question 57

You need to provide multiple teams with secure access to a Cloud Storage bucket, where some require read-only access, some require read-write access, and some require temporary access. Which solution is most appropriate?

A) Share bucket credentials directly
B) Use IAM roles and signed URLs
C) Enable public access to the bucket
D) Use Cloud Storage Transfer Service

Answer B) Use IAM roles and signed URLs

Explanation

A) Sharing bucket credentials directly is insecure. Anyone with the credentials can access the bucket with full permissions, potentially modifying or deleting objects. It also complicates auditing, revocation, and management, making it unsuitable for multi-team environments.

B) Using IAM roles combined with signed URLs provides secure, flexible access control. IAM roles allow fine-grained permissions, such as read-only or read-write access, and can be applied to individual users or groups. Signed URLs allow temporary access to objects without creating IAM accounts and can be configured to expire automatically. This combination supports least-privilege access, auditing, temporary access, and scalability for multiple teams, ensuring secure and operationally efficient collaboration.

C) Enabling public access to the bucket allows anyone to access the data without authentication. This approach is extremely insecure and does not support differentiated access levels or temporary access. It is unsuitable for enterprise workloads.

D) Cloud Storage Transfer Service is designed for bulk transfers of data between storage systems. It does not provide user-level access control, temporary access, or secure collaboration features.

The correct solution is IAM roles and signed URLs. This approach provides fine-grained, secure, and temporary access while minimizing operational risk and maintaining compliance with least-privilege and security best practices.

Question 58

You need to implement a real-time analytics pipeline that ingests events from multiple sources, performs transformations, and stores results in BigQuery for reporting. Which services should you use?

A) Cloud Functions and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery
C) Cloud Run and Cloud SQL
D) Compute Engine and Cloud Bigtable

Answer B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery

Explanation

A) Cloud Functions and Cloud Storage can handle event-driven workflows where individual files are uploaded. While serverless and simple to implement, they are not suitable for high-throughput, real-time streaming from multiple sources. Cloud Functions do not efficiently handle complex transformations, aggregations, or windowed computations, making them less suitable for large-scale analytics.

B) Cloud Pub/Sub, Cloud Dataflow, and BigQuery form a complete, fully managed solution for real-time analytics. Pub/Sub provides scalable, reliable event ingestion with at-least-once delivery. Dataflow allows complex transformations, filtering, aggregation, and windowing. BigQuery stores the processed data for fast querying and reporting. This architecture is scalable, fault-tolerant, and fully serverless, providing near real-time analytics with minimal operational overhead. It also integrates with monitoring, logging, and security services for production-grade deployments.

C) Cloud Run and Cloud SQL provide a containerized serverless environment with relational database storage. While Cloud Run scales automatically, Cloud SQL is not optimized for high-throughput streaming analytics. Using this combination for real-time ingestion and transformation may create performance bottlenecks and operational challenges.

D) Compute Engine and Cloud Bigtable offer flexibility and high throughput for custom workloads, but Compute Engine requires manual orchestration and scaling for streaming pipelines. Bigtable is optimized for NoSQL workloads and does not support analytical querying like BigQuery, making it less suitable for near real-time analytics.

The correct solution is Cloud Pub/Sub, Cloud Dataflow, and BigQuery. This combination provides a scalable, serverless, real-time analytics pipeline with ingestion, transformation, and storage capabilities, reducing operational complexity and ensuring reliable insights.

Question 59

You need to monitor multiple Compute Engine instances for CPU, memory, and disk usage and automatically notify the operations team if thresholds are exceeded. Which service should you use?

A) Cloud Logging
B) Cloud Monitoring with alerting policies
C) Cloud Trace
D) Cloud Storage notifications

Answer B) Cloud Monitoring with alerting policies

Explanation

A) Cloud Logging collects logs from Compute Engine and other services for auditing and troubleshooting. While valuable for historical analysis, it is not designed for real-time system monitoring or threshold-based alerts for CPU, memory, or disk usage. Implementing alerts using logging requires complex custom pipelines, increasing operational overhead.

B) Cloud Monitoring provides real-time collection of system metrics from Compute Engine instances, including CPU utilization, memory consumption, and disk I/O. Alerting policies allow thresholds to be defined and notifications sent to email, Slack, PagerDuty, or other channels. Dashboards provide visualization of trends, enabling proactive capacity planning and operational response. Cloud Monitoring ensures that operations teams can respond promptly, maintain high availability, and adhere to enterprise observability best practices.

C) Cloud Trace is intended for performance monitoring and latency tracking in distributed applications. While useful for identifying bottlenecks in requests, it does not monitor system-level metrics or trigger infrastructure alerts.

D) Cloud Storage notifications alert users about changes to storage objects. They are unrelated to Compute Engine system metrics and cannot provide threshold-based alerts.

The correct solution is Cloud Monitoring with alerting policies because it provides real-time monitoring, automated notifications, visualization, and operational insights for Compute Engine instances, enabling proactive incident response and ensuring high availability and reliability.

Question 60

You need to implement a disaster recovery plan for a mission-critical application that must remain available during a regional outage. Which architecture is most appropriate?

A) Single-region deployment with automated backups
B) Multi-region deployment with active-active instances
C) Single-region deployment with snapshots
D) Deploy all resources in a private VPC

Answer B) Multi-region deployment with active-active instances

Explanation

A) Single-region deployment with automated backups allows recovery from accidental deletion or corruption but does not protect against region-wide outages. If the region fails, downtime is unavoidable until resources are restored in another region, violating near-zero downtime requirements.

B) Multi-region deployment with active-active instances ensures continuous availability across multiple regions. Traffic is distributed using a global load balancer, and healthy instances in other regions automatically handle requests if one region fails. This architecture meets strict recovery objectives (RTO and RPO), minimizes downtime, and supports operational continuity. Active-active deployments also allow for load balancing, scalability, and fault tolerance, making it ideal for mission-critical applications.

C) Single-region deployment with snapshots provides recovery from local failures but requires manual restoration in another region. This introduces downtime and does not offer automated failover, which is insufficient for critical applications.

D) Deploying all resources in a private VPC enhances security but does not provide cross-region redundancy. If the region fails, all resources are unavailable, making this approach unsuitable for disaster recovery objectives.

The correct solution is multi-region deployment with active-active instances. This architecture ensures redundancy, automated failover, and near-zero downtime, providing resilience, operational continuity, and high availability for mission-critical applications while following cloud-native disaster recovery best practices.