Google Associate Cloud Engineer Exam Dumps and Practice Test Questions Set 2  Q 21- 40

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Question 21

You need to deploy a stateless web application on Google Cloud that must scale automatically based on request load and be highly available across multiple zones. Which service is the best choice?

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

Answer A) Compute Engine with managed instance group

Explanation

A) Compute Engine with a managed instance group allows you to deploy multiple instances of a VM template and automatically manage scaling, health checks, and instance replacement. Managed instance groups support auto-scaling based on metrics such as CPU usage, load balancing requests, or custom metrics. This ensures that the web application can handle varying traffic loads effectively. By configuring the instance group across multiple zones, you achieve high availability, as traffic can be rerouted to healthy instances in other zones in case of failures. Load balancing distributes traffic evenly across instances, and health checks ensure that unhealthy instances are replaced automatically. This approach provides flexibility for stateless applications and fine-grained control over the environment, making it an ideal solution for highly available, auto-scaling workloads.

B) App Engine Standard Environment provides a fully managed serverless platform with automatic scaling and HTTPS, which is excellent for simple web applications. However, App Engine Standard has restrictions on supported runtimes and execution patterns, and for applications requiring full VM-level control or custom OS configurations, App Engine might not suffice. Additionally, while it offers auto-scaling, developers may have limited control over underlying infrastructure, which can be a limitation for certain enterprise workloads.

C) Cloud Run is designed for containerized applications and provides serverless scaling and stateless execution. It can automatically scale based on HTTP requests, which makes it highly efficient for microservices or containerized workloads. However, Cloud Run’s scale-to-zero behavior may introduce slight cold start latency in high-traffic scenarios. It is excellent for microservices, but for full control over VM-level configurations or persistent workloads with predictable latency, Compute Engine managed instance groups may be more appropriate.

D) Kubernetes Engine (GKE) offers container orchestration, auto-scaling, and high availability. While it can handle stateless applications with multiple replicas and load balancing, GKE introduces operational complexity with cluster management, node pools, and ingress configuration. For teams looking for straightforward VM-based stateless deployment with minimal operational overhead, Compute Engine managed instance groups provide simpler, highly available, auto-scaling solutions without the need for container orchestration.

Compute Engine managed instance groups are correct because they combine VM-level control, health monitoring, automatic scaling, and multi-zone deployment, ensuring high availability and reliability for stateless applications. They allow teams to define scaling policies based on metrics, maintain redundancy across zones, and manage load efficiently while retaining flexibility to customize VM environments as needed. This architecture aligns with cloud-native principles while providing a robust foundation for enterprise-grade applications.

Question 22

You need to provide users with temporary access to a set of Cloud Storage objects without creating additional IAM accounts. Which approach is the most secure and efficient?

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

Answer C) Use signed URLs

Explanation

A) Sharing your user credentials is highly insecure and violates the principle of least privilege. Anyone with your credentials can access all resources in the project, potentially leading to unauthorized changes or data exposure. Sharing credentials also makes auditing and revocation difficult and is considered a critical security risk in enterprise environments.

B) Creating a service account with long-lived keys provides programmatic access to resources but is not ideal for temporary access. Long-lived keys require careful rotation and monitoring, and sharing them with users or third parties increases the risk of credential compromise. While service accounts are suitable for automated applications, they do not solve the need for short-term, user-level access with minimal operational overhead.

C) Signed URLs allow secure, temporary access to individual Cloud Storage objects without creating additional IAM accounts. You can generate a URL that is valid for a specific duration (minutes, hours, or days) and provides access only to the specified object. Users can access the object directly using the URL, and the access automatically expires after the defined period. This method ensures security by limiting both scope and duration, and it does not require credential sharing or permanent account creation. Signed URLs are auditable, easily revoked by changing object permissions, and scalable for external collaborations.

D) Granting Owner permissions to all users is extremely unsafe. Owner permissions provide full control over the project, including the ability to modify IAM policies, delete resources, and access sensitive data. This approach exposes the project to significant security risks, violates the principle of least privilege, and is unsuitable for temporary access requirements.

The correct approach is signed URLs because they provide secure, time-limited access to specific objects without sharing credentials or creating permanent accounts. This approach reduces operational complexity, ensures fine-grained access control, and supports auditing and compliance requirements. Signed URLs enable secure collaboration with external users while maintaining centralized control over Cloud Storage permissions, balancing convenience and security effectively.

Question 23

Your organization wants to migrate multiple workloads from on-premises servers to Google Cloud, including web servers, databases, and batch processing applications. Which approach ensures minimal disruption and allows for phased migration?

A) Lift-and-shift using Compute Engine only
B) Using Database Migration Service for databases and Compute Engine for VMs
C) Exporting everything to Cloud Storage and recreating manually
D) Deploying all workloads in App Engine

Answer B) Using Database Migration Service for databases and Compute Engine for VMs

Explanation

A) Lift-and-shift using Compute Engine only involves copying VMs from on-premises servers to Google Cloud. While this can quickly move workloads, it does not address database migration challenges, scaling requirements, or optimization for cloud-native features. Without proper database migration tools, there is a risk of downtime, data loss, or compatibility issues, especially for production databases requiring continuous availability.

B) Using Database Migration Service (DMS) for database workloads ensures continuous replication from on-premises databases to Cloud SQL, minimizing downtime and preserving data integrity. Compute Engine can be used for web servers, batch processing, and other workloads that require VM-level control. This approach allows a phased migration where teams migrate databases first with minimal disruption, then gradually transition application workloads. It ensures operational continuity while leveraging cloud-native services for scalability, monitoring, and automation. DMS handles schema migration, initial data seeding, and ongoing replication, providing near-zero downtime for critical databases. Compute Engine supports customization for application workloads, allowing teams to replicate production environments efficiently.

C) Exporting everything to Cloud Storage and manually recreating workloads is inefficient and prone to errors. This method requires manual recreation of VMs, databases, and services in Google Cloud, increasing operational overhead, migration time, and risk of downtime. It also does not support continuous replication, making it unsuitable for production workloads with minimal downtime requirements.

D) Deploying all workloads in App Engine is possible for web applications, but not all workloads are compatible with App Engine. Batch processing jobs and database services often require VM-level access, persistent storage, or custom software stacks that App Engine does not support. Attempting to migrate all workloads to App Engine would require significant redesign, introducing delays and complexity.

The correct approach is using DMS for databases and Compute Engine for VMs. This ensures minimal disruption, supports phased migration, and provides flexibility for both cloud-native and legacy workloads. DMS handles data replication reliably, while Compute Engine maintains application continuity, enabling organizations to transition workloads efficiently without sacrificing availability or operational stability.

Question 24

You need to implement a monitoring solution that alerts your operations team when CPU usage exceeds 80% or disk usage exceeds 90% on Compute Engine instances. Which service and configuration are most appropriate?

A) Cloud Logging with export to BigQuery
B) Cloud Monitoring with alerting policies
C) Cloud Trace with distributed tracing
D) Cloud Storage with notifications

Answer B) Cloud Monitoring with alerting policies

Explanation

A) Cloud Logging collects logs from Compute Engine instances and other resources, allowing event analysis and historical auditing. While logs may contain performance-related entries, Cloud Logging does not provide native real-time alerting on system metrics such as CPU or disk usage. Exporting logs to BigQuery allows post-facto analysis but does not support real-time alerts, which is critical for operations teams needing immediate notification of threshold breaches.

B) Cloud Monitoring is specifically designed for real-time metric collection and alerting. By configuring alerting policies, you can define conditions such as CPU usage exceeding 80% or disk utilization exceeding 90%. Cloud Monitoring automatically evaluates these metrics and triggers notifications to email, Slack, PagerDuty, or other channels, ensuring the operations team can respond promptly. Additionally, Cloud Monitoring integrates dashboards, visualization, and historical trend analysis, which helps identify performance patterns, potential bottlenecks, and capacity planning needs. Alerts can be customized with multiple conditions, thresholds, and documentation messages for operational clarity. This solution provides automated, real-time monitoring and proactive notifications, reducing downtime and operational risk.

C) Cloud Trace focuses on distributed application performance, measuring latency and bottlenecks across services. While useful for identifying slow requests or transaction delays, Cloud Trace does not monitor system-level metrics such as CPU or disk usage. Therefore, it cannot provide real-time alerting for infrastructure performance thresholds.

D) Cloud Storage with notifications is designed to alert applications or users when objects are created, deleted, or updated. It is unrelated to system monitoring or performance metrics and cannot detect CPU or disk usage conditions.

The correct solution is Cloud Monitoring with alerting policies because it directly monitors system metrics, evaluates thresholds in real time, and sends automated notifications to operational teams. This approach ensures proactive management, reduces response times to performance issues, and supports operational excellence by providing historical context and visualization alongside alerting capabilities. Cloud Monitoring enables a scalable, centralized, and automated monitoring strategy for Compute Engine and other Google Cloud resources.

Question 25

You want to deploy a BigQuery pipeline that aggregates daily sales data from multiple sources and provides reports to business users. You also need to minimize storage costs and optimize query performance. Which configuration is most appropriate?

A) Store raw data in BigQuery Standard tables with daily queries
B) Store raw data in Cloud Storage, then use BigQuery external tables
C) Load data into BigQuery partitioned tables with clustering
D) Use Cloud SQL for daily aggregations and export to BigQuery

Answer C) Load data into BigQuery partitioned tables with clustering

Explanation

A) Storing raw data in BigQuery Standard tables is straightforward and allows running SQL queries on the dataset. However, as data grows, queries may scan large amounts of data, increasing cost and reducing performance. Standard tables without partitioning or clustering can become inefficient for large datasets and repetitive daily aggregations, leading to higher operational costs and slower query response times.

B) Storing raw data in Cloud Storage and using BigQuery external tables allows queries without loading data into BigQuery. While this reduces storage costs, external tables can have slower query performance compared to native BigQuery tables. For frequent daily aggregations and reporting, external tables may introduce latency and inefficiencies, making them suboptimal for business-critical reporting pipelines.

C) Loading data into BigQuery partitioned tables with clustering provides the optimal balance of cost efficiency and query performance. Partitioning organizes data by a column such as date, enabling queries to scan only relevant partitions rather than the entire dataset. Clustering organizes data within partitions based on commonly queried columns, improving performance for filter and aggregation operations. This configuration reduces query costs by minimizing scanned data and accelerates reporting for business users. Additionally, partitioned tables integrate seamlessly with scheduled queries, automation, and data pipelines, enabling efficient daily aggregations and timely report generation. This approach is a cloud-native best practice for analytics pipelines.

D) Using Cloud SQL for daily aggregations and exporting to BigQuery is inefficient for large-scale analytical workloads. Cloud SQL is optimized for transactional workloads and may struggle with large aggregations, leading to slow performance. Exporting data from Cloud SQL to BigQuery adds operational overhead and latency, making it less suitable for daily reporting pipelines.

The correct solution is BigQuery partitioned tables with clustering. Partitioning reduces storage scanning costs, clustering improves query performance for aggregation operations, and BigQuery’s serverless architecture ensures scalability. This configuration enables timely reporting, cost optimization, and high-performance analytics for business users, providing an efficient, cloud-native data pipeline.

Question 26

You are designing a secure architecture for a Google Cloud project where certain sensitive workloads need to communicate privately with each other across multiple VPCs without exposing traffic to the public internet. Which approach is most appropriate?

A) VPC Peering
B) Cloud VPN
C) Shared VPC
D) Cloud NAT

Answer A) VPC Peering

Explanation

A) VPC Peering allows private connectivity between two VPC networks using internal IP addresses. Traffic between peered VPCs stays on Google’s network and does not traverse the public internet. This makes it highly secure and low-latency, ideal for sensitive workloads that need direct communication. VPC Peering is point-to-point, meaning two VPCs can exchange traffic privately while retaining their own control over resources, subnets, and policies. It also supports multiple peering connections across projects, enabling a scalable network architecture for large enterprises. Peering does not introduce complex configurations like VPNs and ensures consistent, private, high-speed communication between VPCs. This makes it an optimal solution for scenarios where internal communication must remain isolated from the public network while maintaining high performance and security.

B) Cloud VPN provides encrypted tunnels over the public internet. While secure, it exposes traffic to potential internet routing issues and adds latency compared to private Google network connections. VPNs are better suited for hybrid connectivity between on-premises networks and Google Cloud rather than internal private communication between VPCs. Using VPNs for internal VPC communication adds unnecessary complexity and operational overhead, including key rotation and monitoring of tunnel health.

C) Shared VPC allows multiple projects to connect to a single host VPC, centralizing networking and firewall management. While this supports controlled connectivity between service projects and the host network, it does not establish private, direct links between VPCs in separate organizations or projects without additional configuration. It is more about sharing resources than creating point-to-point private connectivity. For VPCs in different administrative boundaries, Shared VPC alone cannot replace peering for secure, private communication.

D) Cloud NAT enables instances in private subnets to access the internet without exposing internal IP addresses. While useful for outbound connectivity, NAT does not provide private communication between VPCs. It cannot connect workloads across networks and is therefore not applicable for establishing private inter-VPC communication.

The correct choice is VPC Peering because it ensures private, high-speed, low-latency connectivity directly between VPCs without traversing the public internet. It provides secure communication for sensitive workloads while maintaining network isolation, scalability, and operational simplicity. VPC Peering is widely used in multi-project and multi-team environments to connect microservices, databases, and application workloads securely and efficiently. By keeping traffic within Google’s network, organizations benefit from increased security, reliability, and performance, meeting best practices for cloud network architecture.

Question 27

You are tasked with migrating a large on-premises PostgreSQL database to Cloud SQL with minimal downtime. Which approach ensures continuous replication and near-zero disruption to production applications?

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

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

Explanation

A) Exporting the database to a SQL dump and importing it into Cloud SQL is suitable for small or non-critical workloads but introduces downtime. The production database must stop updates during export to ensure data consistency, and any changes made after export are lost. This process can take hours or even days for large datasets, making it unsuitable for critical systems where continuous availability is required.

B) Database Migration Service (DMS) supports continuous replication from on-premises PostgreSQL databases to Cloud SQL. It seeds the target database, then continuously replicates changes in near real-time. This enables minimal downtime migration: applications can continue operating while replication occurs, and a final cutover can be scheduled at a convenient time. DMS handles schema migration, data validation, and automatic retries in case of network interruptions. This method ensures consistency and integrity of the database while minimizing operational disruption. DMS also integrates with monitoring tools, providing visibility into migration progress and potential issues, which is critical for production-grade migrations.

C) Manual schema creation and data copy is error-prone and time-consuming. Maintaining synchronization between source and target databases requires custom scripts, monitoring, and manual intervention. This approach cannot guarantee minimal downtime and is operationally complex, increasing the risk of inconsistencies or failures during migration. It is not suitable for large, production-critical databases.

D) Cloud Storage Transfer Service is designed for moving files between storage locations, not for relational database migration. It cannot handle schema, transactional integrity, or continuous replication. Using it for database migration would result in incomplete or inconsistent data and is therefore inappropriate for this scenario.

The correct approach is Database Migration Service because it allows near-zero downtime migration with continuous replication. It ensures data consistency, reliability, and integrity while allowing production applications to remain online. DMS simplifies the migration process, reduces operational risk, and supports enterprise-level planning and auditing requirements for critical workloads.

Question 28

You are designing a cost-effective long-term storage solution for compliance logs that are rarely accessed. Which Google Cloud Storage class should you use?

A) Standard
B) Nearline
C) Coldline
D) Archive

Answer D) Archive

Explanation

A) Standard storage is intended for frequently accessed data. It provides low-latency, high-throughput access, but comes at a higher cost. Using Standard for logs that are rarely accessed is cost-inefficient, especially for multi-year retention, as it significantly increases storage expenditure without benefiting access speed.

B) Nearline storage is for data accessed approximately once per month. It is more cost-effective than Standard and suitable for backup or disaster recovery scenarios. However, for logs retained over multiple years and accessed very infrequently, Nearline may still incur unnecessary costs compared to deeper archival solutions.

C) Coldline storage is optimized for data accessed infrequently, roughly once per quarter. While cheaper than Nearline, Cloud Coldline may still incur higher storage costs and slightly higher retrieval costs for data that is rarely accessed over many years. It is suitable for data with occasional access but not optimal for long-term archival retention.

D) Archive storage is specifically designed for long-term retention of rarely accessed data, such as compliance logs, regulatory records, and archival datasets. It provides the lowest storage cost among all Google Cloud Storage classes while maintaining high durability. Retrieval times are slightly longer, which is acceptable for archival workloads where immediate access is not required. Archive storage also supports lifecycle management policies, enabling automatic transitions from other storage classes to Archive after a defined period. This minimizes operational effort while ensuring compliance and cost efficiency.

The correct choice is Archive storage because it provides a durable, cost-effective solution for retaining logs for several years. It balances low storage costs with acceptable retrieval performance and supports lifecycle automation. By using Archive, organizations can meet compliance requirements while minimizing operational and financial overhead. This aligns with best practices for cloud-native long-term storage strategies, ensuring security, durability, and predictable cost management over multi-year retention periods.

Question 29

You want to provide secure access to multiple Cloud SQL instances from a Kubernetes Engine cluster without exposing database credentials in plaintext. Which approach is most appropriate?

A) Store credentials in plaintext in ConfigMaps
B) Use Kubernetes Secrets with service accounts
C) Share root database credentials with all applications
D) Hard-code passwords in container images

Answer B) Use Kubernetes Secrets with service accounts

Explanation

A) Storing credentials in plaintext in ConfigMaps is insecure because ConfigMaps are not encrypted by default and can be accessed by any pod with access to the ConfigMap. This exposes sensitive database credentials to potential misuse or accidental disclosure. It violates the principle of least privilege and cloud security best practices.

B) Kubernetes Secrets provide a secure mechanism for storing sensitive information such as database credentials, API keys, or certificates. Secrets are stored in an encrypted format within the cluster and can be mounted into pods or injected as environment variables. By combining Secrets with service accounts, you can control which pods have access to specific credentials, ensuring that only authorized workloads can access Cloud SQL instances. Kubernetes RBAC policies can further restrict access, providing a robust security posture. This approach prevents plaintext exposure, supports credential rotation, and integrates seamlessly with Cloud SQL IAM authentication for additional security.

C) Sharing root database credentials with all applications is highly insecure. It gives unnecessary privileges to all workloads, increasing the risk of accidental or malicious data changes. This approach violates security principles, including least privilege and separation of duties, and is unsuitable for production environments.

D) Hard-coding passwords in container images is insecure because images may be stored in registries, shared across environments, or inspected during deployment. Exposing credentials in this way introduces a significant security risk and complicates rotation or revocation of passwords.

The correct approach is using Kubernetes Secrets combined with service accounts. This ensures that database credentials are securely stored, encrypted, and only accessible to authorized workloads. It supports operational best practices such as secret rotation, auditing, and minimal privilege assignment while preventing accidental exposure or misuse. This method aligns with cloud-native security standards for integrating Kubernetes workloads with Cloud SQL.

Question 30

You are designing a multi-region disaster recovery solution for a critical application hosted on Google Cloud. The application requires near-zero downtime in the event of a region-wide failure. Which architecture best satisfies this requirement?

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 recoverability from data loss but does not protect against a region-wide outage. If the region fails, the application will be unavailable until it is restored in another region. Recovery may take hours or days, violating near-zero downtime requirements.

B) Multi-region deployment with active-active instances ensures continuous availability even if one region fails. Traffic is distributed across multiple regions using a global load balancer. Each region runs active instances that can handle user requests independently. In case of a failure, traffic automatically shifts to healthy regions without disruption. This approach minimizes downtime and meets stringent recovery objectives (RTO and RPO). Active-active architecture also provides resilience, high availability, and scalability while maintaining performance for end users. It is a cloud-native best practice for mission-critical applications requiring disaster tolerance.

C) Single-region deployment with snapshots allows data recovery but requires manual restoration to a different region, introducing significant downtime. It does not provide automatic failover, and RTO objectives are unlikely to be met. Snapshots alone cannot prevent disruption during a regional failure.

D) Deploying all resources in a private VPC enhances network isolation and security but does not provide regional redundancy. If the region hosting the private VPC becomes unavailable, the application will be inaccessible. VPC isolation alone does not address disaster recovery or high availability requirements.

The correct architecture is a multi-region deployment with active-active instances. It ensures redundancy, automatic failover, and near-zero downtime. By leveraging global load balancing, health checks, and distributed infrastructure, organizations achieve resilience against region-wide outages while maintaining operational continuity, performance, and user satisfaction. This design aligns with cloud-native disaster recovery best practices and enterprise-level availability objectives.

Question 31

You are tasked with setting up a highly available, auto-scaling containerized application in Google Cloud that should scale automatically based on HTTP request load and support zero-downtime deployments. Which service is most appropriate?

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 allows you to deploy multiple VM instances from a template and configure auto-scaling. While it can handle stateless applications and provide high availability with multi-zone deployment, it is not natively container-focused. Deploying containerized applications on Compute Engine requires manually managing container runtimes, orchestration, load balancing, and rolling updates, which increases operational complexity. For container workloads requiring seamless scaling and zero-downtime deployments, relying solely on managed instance groups is suboptimal.

B) App Engine Standard Environment offers a fully managed platform with automatic scaling and built-in HTTPS support. It is excellent for simple containerized or application deployments with minimal management. However, it has limitations on custom runtimes, execution environments, and container orchestration. App Engine Standard is best suited for applications that fit within the provided runtime environment and does not offer fine-grained control over container orchestration, deployments, and scaling policies. For complex containerized applications with zero-downtime rolling deployments, App Engine may not provide sufficient flexibility.

C) Cloud Run is a serverless container platform that automatically scales containers based on HTTP requests. It supports stateless applications, pay-per-use pricing, and scale-to-zero. While Cloud Run is highly efficient for microservices or lightweight applications, it has some limitations with complex orchestration, inter-container networking, and persistent storage. Cloud Run does not provide the same level of deployment control as Kubernetes Engine for managing multiple containerized services with dependencies, zero-downtime updates, and multi-container orchestration.

D) Kubernetes Engine (GKE) is a fully managed Kubernetes service that provides orchestration, auto-scaling, high availability, and rolling updates for containerized applications. GKE supports zero-downtime deployments using strategies such as rolling updates and canary releases. Multi-zone or multi-region clusters ensure high availability, and GKE’s native integration with load balancing, monitoring, and IAM provides operational control over security, performance, and scaling. GKE allows teams to deploy multiple containers with complex interdependencies while maintaining observability, making it ideal for enterprise-grade containerized workloads requiring automated scaling, high availability, and zero-downtime deployments.

The correct choice is Kubernetes Engine because it combines container orchestration, rolling updates, multi-zone high availability, and automated scaling. It supports zero-downtime deployments, complex container workflows, and secure operations while enabling teams to deploy production-ready applications efficiently. For highly available containerized applications in Google Cloud, GKE provides the best combination of control, scalability, and operational efficiency.

Question 32

You are designing a pipeline to ingest real-time streaming data from IoT devices into BigQuery for analytics. The pipeline must handle variable load, scale automatically, and transform data in transit. 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 workloads, such as processing individual files uploaded to Cloud Storage. While serverless and easy to deploy, this approach does not efficiently support high-throughput streaming data or real-time transformations. Cloud Functions lack native batching, windowing, and scaling for variable IoT data streams, making it challenging to process large, continuous streams without creating multiple orchestrated functions.

B) Cloud Pub/Sub is a messaging service that ingests real-time data from IoT devices. It provides at-least-once delivery, buffering, and horizontal scaling to handle variable load. Cloud Dataflow is a fully managed data processing service that supports batch and stream processing using Apache Beam. It enables transformations such as aggregations, filtering, and enrichment in real time. BigQuery serves as the analytical data warehouse for storing and querying the transformed data. This combination creates a fully serverless, auto-scaling, end-to-end pipeline that ingests, processes, and analyzes streaming data efficiently. It allows teams to handle variable load, implement data transformations, and generate analytics in near real time without managing infrastructure.

C) Cloud Run and Cloud SQL provide a containerized execution environment and relational database storage. Cloud Run is serverless and can scale automatically, but Cloud SQL is not optimized for high-throughput streaming workloads. Handling IoT data in Cloud SQL introduces latency and operational challenges, making it unsuitable for real-time analytics pipelines at scale.

D) Compute Engine and Cloud Bigtable offer flexibility and high-throughput storage for NoSQL workloads. However, Compute Engine requires manual orchestration for streaming data, scaling, and processing, adding significant operational overhead. Cloud Bigtable is optimized for high-volume key-value storage but does not provide analytical query capabilities like BigQuery, which is necessary for end-to-end analytics.

The correct solution is Cloud Pub/Sub, Cloud Dataflow, and BigQuery because they provide an end-to-end, fully managed, scalable, and efficient pipeline for real-time IoT data. Pub/Sub handles ingestion, Dataflow performs transformations and stream processing, and BigQuery provides fast analytics. This architecture supports variable load, near-zero operational overhead, and cloud-native scalability while providing detailed monitoring and observability for production workloads.

Question 33

You need to provide temporary, secure access to a third-party vendor for uploading files to a Cloud Storage bucket. Which approach is most appropriate?

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

Answer C) Use signed URLs

Explanation

A) Sharing user credentials is insecure and violates the principle of least privilege. Anyone with your credentials gains full access to all project resources, which risks accidental or malicious changes. It also complicates auditing and revocation. Sharing credentials is never recommended in production environments.

B) Creating a long-lived service account key provides programmatic access but is unsuitable for temporary access. It requires careful key management, rotation, and monitoring. Sharing the key exposes the project to unnecessary security risks if the key is misused or compromised.

C) Signed URLs provide secure, temporary access to specific Cloud Storage objects or buckets without creating additional IAM accounts. You can define expiration times, limiting access to minutes, hours, or days. Signed URLs allow third-party vendors to upload or download files without exposing credentials or granting permanent permissions. This method is secure, auditable, and aligns with cloud-native security best practices. It also allows fine-grained control, restricting access to specific objects and ensuring compliance with least-privilege principles.

D) Granting Owner permissions to all users is highly insecure. Owner permissions provide full access to the project, including the ability to modify IAM policies, delete resources, or access sensitive data. This approach is excessive, violates security principles, and should never be used for temporary access scenarios.

The correct approach is signed URLs because they balance security, flexibility, and operational efficiency. They enable third-party access for a limited duration without exposing sensitive credentials or granting unnecessary permissions. Signed URLs are auditable, easy to revoke, and fully compatible with Cloud Storage best practices for temporary and controlled access.

Question 34

You need to monitor Compute Engine instances for CPU utilization, disk I/O, and memory usage, and receive alerts when thresholds are exceeded. Which service is best suited for this requirement?

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, allowing analysis of events and troubleshooting. While useful for historical auditing, it does not provide real-time monitoring or automated alerts for system metrics such as CPU utilization or disk I/O. Exporting logs to BigQuery enables analysis, but real-time threshold-based alerting is not supported natively in Cloud Logging.

B) Cloud Monitoring collects metrics from Compute Engine, including CPU usage, memory utilization, and disk I/O. Alerting policies allow defining thresholds and sending notifications via email, Slack, or PagerDuty when metrics exceed limits. Cloud Monitoring supports dashboards, visualization, and historical trend analysis, enabling teams to proactively identify and address performance issues. Alerts can include multiple conditions, ensuring granular monitoring and operational visibility. This solution is fully managed, scalable, and provides real-time notification for operational teams, reducing downtime and ensuring high availability.

C) Cloud Trace is designed to monitor application performance and latency across services. While it provides valuable insights into request-level performance, it does not monitor system-level metrics such as CPU or disk usage and cannot trigger threshold-based alerts for infrastructure.

D) Cloud Storage notifications alert users to changes in storage objects, such as creation or deletion. This service is unrelated to Compute Engine metrics or performance monitoring, and cannot provide CPU, memory, or disk usage alerts.

The correct solution is Cloud Monitoring with alerting policies. It provides real-time, automated monitoring and notification for system metrics. This ensures operational teams can respond promptly to performance issues, maintain high availability, and perform capacity planning effectively. Cloud Monitoring supports scalable, centralized observability for all Compute Engine instances and integrates seamlessly with other Google Cloud monitoring and alerting workflows.

Question 35

You need to implement a disaster recovery strategy for a mission-critical application that must remain available even in the event of 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 durability but does not protect against regional outages. If the entire region fails, the application will be unavailable until it is restored in another region. Recovery may take hours or even days, violating near-zero downtime requirements for mission-critical applications.

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 unaffected regions handle requests automatically in the event of a regional outage. This architecture meets strict recovery objectives (RTO and RPO), providing resilience, high availability, and minimal disruption to users. Active-active deployments also allow load distribution, disaster tolerance, and operational continuity, making them suitable for enterprise-grade applications with high availability requirements.

C) Single-region deployment with snapshots allows for recovery, but restoring applications to a different region is manual and time-consuming. This introduces downtime, failing to meet near-zero availability requirements for critical workloads. Snapshots alone do not provide automated failover or regional redundancy.

D) Deploying all resources in a private VPC enhances security and network isolation but does not provide redundancy against regional failures. If the region fails, the application and all resources become unavailable. VPC isolation alone does not satisfy disaster recovery or high-availability requirements.

The correct solution is a multi-region deployment with active-active instances. It ensures redundancy, automatic failover, and near-zero downtime. By distributing workloads across regions, leveraging global load balancing, and implementing health checks, organizations can maintain high availability, operational continuity, and resilience against catastrophic failures. This architecture aligns with cloud-native best practices for mission-critical applications requiring continuous uptime.

Question 36

You need to migrate an on-premises Oracle database to Google 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 Oracle database to a SQL dump and importing into Cloud SQL is suitable for small datasets, test environments, or non-production workloads. However, this method introduces significant downtime because the source database must stop updates during the export process to ensure data consistency. Any transactions that occur after the export will not be captured, which makes this approach unsuitable for production environments requiring minimal downtime. Additionally, for large datasets, export and import times may be extensive, further impacting availability and operational continuity.

B) Database Migration Service (DMS) provides a fully managed solution for migrating databases, including Oracle, to Cloud SQL with minimal downtime. It supports continuous replication, which captures ongoing changes in near 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 operating with minimal disruption. DMS also provides monitoring, logging, and error-handling features that enhance reliability and transparency during the migration process. This approach is suitable for mission-critical workloads that require high availability and consistency throughout the migration.

C) Manual schema creation and data copy is time-consuming, prone to human error, and operationally complex. Ensuring data consistency between the source Oracle database and Cloud SQL requires writing custom scripts, continuous monitoring, and manual interventions. This approach is unsuitable for minimal downtime migration and carries a higher risk of data loss or inconsistency during the process.

D) Cloud Storage Transfer Service is designed for bulk file transfers between storage systems, such as migrating files from on-premises storage to Cloud Storage. While useful for storage migration, it cannot handle relational database schemas, transactional integrity, or continuous replication. Therefore, it is not applicable for database migration scenarios requiring minimal downtime and high consistency.

The correct approach is using Database Migration Service because it enables continuous replication, reduces operational overhead, maintains data integrity, and supports near-zero downtime migration for production workloads. DMS provides automation, monitoring, and error handling that ensures a smooth migration process while allowing the source database to remain operational. This solution aligns with cloud-native best practices for enterprise database migrations, combining security, reliability, and operational efficiency.

Question 37

You need to provide secure access to a Cloud Storage bucket for multiple teams, where some need read-only access, some read-write, and some temporary access. Which approach 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 highly insecure and violates the principle of least privilege. Anyone with access to shared credentials can perform any operation allowed by the credentials, potentially deleting, modifying, or exposing data. It also complicates auditing and access revocation, creating significant security risks. For multi-team environments with varying access levels, sharing credentials is neither scalable nor manageable.

B) Using IAM roles combined with signed URLs provides a secure and flexible solution. IAM roles allow fine-grained access control, assigning permissions such as Storage Object Viewer for read-only access or Storage Object Admin for read-write access. Signed URLs allow temporary access to objects without creating additional IAM accounts. These URLs can be configured to expire after a defined period, providing temporary, auditable access for external collaborators or contractors. This approach ensures security, scalability, and compliance while minimizing operational overhead. Teams can access only what they need, credentials are not exposed, and temporary access is automatically revoked when URLs expire.

C) Enabling public access to the bucket allows anyone with the link to view or modify objects, depending on permissions. This approach is extremely insecure for internal or sensitive workloads. It does not allow differentiated access levels, temporary access, or auditing, making it unsuitable for enterprise scenarios with multiple teams and varying access requirements.

D) Cloud Storage Transfer Service is designed for bulk transfers between storage locations, such as moving data from one bucket to another or from on-premises storage. While useful for migration and replication, it does not provide user-level access control, temporary access, or security management for multiple teams. It does not meet the requirement of providing differentiated, secure access to a bucket.

The correct solution is using IAM roles and signed URLs because this combination provides granular, secure, and temporary access control. IAM ensures permanent team-specific permissions, while signed URLs provide time-bound access for external users or temporary requirements. Together, they support operational security, compliance, and scalability, allowing multiple teams to collaborate safely without exposing sensitive credentials or over-permissioning access.

Question 38

You want to implement a real-time analytics pipeline that ingests events from multiple sources, transforms the data, and stores results in BigQuery for reporting. Which services are 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 process individual events or files as they arrive in Cloud Storage. While serverless and easy to implement for small-scale tasks, this approach is not suitable for high-throughput, real-time streaming from multiple sources. Cloud Functions do not handle complex transformations, aggregations, or windowed computations efficiently, and scaling logic may become complex for large data volumes.

B) Cloud Pub/Sub provides a scalable, reliable messaging system that can ingest real-time events from multiple sources with at-least-once delivery. Cloud Dataflow offers fully managed stream and batch data processing using Apache Beam, allowing complex transformations, aggregations, and enrichment. BigQuery serves as the analytical data warehouse where processed data is stored for reporting and analytics. This combination ensures that real-time data is ingested, processed, and stored efficiently, supporting variable workloads and near-zero operational overhead. The pipeline is scalable, resilient, and cloud-native, allowing for automated monitoring, error handling, and data lineage tracking.

C) Cloud Run and Cloud SQL can process containerized workloads and store results in a relational database. However, Cloud SQL is not optimized for high-throughput streaming analytics, and Cloud Run is better suited for stateless, event-driven microservices rather than full-scale streaming pipelines. This architecture may lead to performance bottlenecks and higher operational complexity when handling large volumes of real-time data.

D) Compute Engine and Cloud Bigtable provide high throughput and flexibility for workloads requiring VMs and NoSQL storage. While Cloud Bigtable can handle large-scale ingestion, it does not offer SQL-based analytics like BigQuery. Compute Engine requires manual orchestration for streaming ingestion and transformation, increasing operational burden and risk.

The correct solution is Cloud Pub/Sub, Cloud Dataflow, and BigQuery because it provides a fully managed, scalable, and serverless solution for real-time analytics. Pub/Sub ensures reliable data ingestion, Dataflow handles complex transformations at scale, and BigQuery supports fast querying and reporting. This architecture minimizes operational overhead, provides near real-time insights, and adheres to cloud-native design principles, making it ideal for enterprise analytics pipelines.

Question 39

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, allowing event analysis and troubleshooting. While it stores valuable information for auditing and debugging, it is not optimized for real-time monitoring of CPU, memory, or disk metrics, nor can it automatically trigger threshold-based alerts for operations teams.

B) Cloud Monitoring collects system-level metrics from Compute Engine, including CPU utilization, memory usage, and disk I/O. Alerting policies allow defining thresholds and sending notifications through multiple channels, such as email, Slack, or PagerDuty. Dashboards provide visualization and trend analysis, enabling teams to proactively address performance issues before they affect applications. Cloud Monitoring supports scalable, real-time monitoring, integrates with logging and auditing tools, and provides actionable insights for maintaining high availability and operational efficiency.

C) Cloud Trace is used for monitoring application-level latency and request performance across distributed services. While valuable for performance optimization, it does not provide system-level metrics monitoring for CPU, memory, or disk usage and cannot send automated threshold-based alerts.

D) Cloud Storage notifications inform users about object creation, deletion, or updates in Cloud Storage. They are unrelated to monitoring system metrics or Compute Engine instance performance and cannot trigger alerts for CPU or disk usage thresholds.

The correct solution is Cloud Monitoring with alerting policies. It provides real-time monitoring, automated alerting, and visualization of system metrics across all Compute Engine instances. This ensures operations teams can respond promptly to performance issues, maintain high availability, and plan capacity effectively. Cloud Monitoring supports enterprise-level observability, operational efficiency, and scalability across Google Cloud environments.

Question 40

You need to design a disaster recovery plan for a mission-critical application requiring near-zero downtime in the event of a regional failure. Which architecture best satisfies this requirement?

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 recoverability from data loss but does not protect against region-wide outages. If the entire region fails, the application will be unavailable until restored in another region. Recovery may take hours or days, violating near-zero downtime requirements.

B) Multi-region deployment with active-active instances ensures high availability across multiple regions. Traffic is distributed using global load balancers, and healthy instances in unaffected regions handle requests automatically if one region fails. This architecture meets strict recovery objectives (RTO and RPO), minimizes downtime, and provides resilience, operational continuity, and load distribution. Active-active deployments are cloud-native best practices for mission-critical applications requiring continuous uptime.

C) Single-region deployment with snapshots allows recovery but requires manual restoration in a different region. This process introduces downtime and cannot meet near-zero availability requirements. Snapshots alone do not provide automated failover or redundancy.

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

The correct architecture is multi-region deployment with active-active instances. It provides redundancy, automatic failover, and near-zero downtime. By distributing workloads across regions, using global load balancing, and implementing health checks, organizations achieve resilience against regional failures while maintaining high availability and operational continuity. This design aligns with best practices for cloud-native disaster recovery.