Google PCA Analyze and optimize technical and business processes Practice Test 3 — 15 Questions

Question 1

A company runs a web application on Compute Engine instances. The application stores session state in files on local SSD. The company wants to reduce costs without sacrificing performance for a predictable traffic pattern. What should they do?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use preemptible VMs in a managed instance group with autoscaling, and store session state in Redis (Memorystore).. Preemptible VMs reduce compute costs by up to 80%, and using a managed instance group with autoscaling handles the predictable traffic pattern efficiently. Storing session state in Redis (Memorystore) instead of local SSD ensures session data persists across VM preemptions and is shared among instances, which is critical for stateful applications. This combination maintains performance while eliminating the cost of always-on VMs.

Answer

Change the machine type to N2 standard and use committed use discounts.

Answer

Keep local SSDs but use sole-tenant nodes to reduce contention.

Answer

Migrate the session state to Cloud Firestore and use preemptible VMs.

Question 2

You are running a Cloud Run service that experiences occasional cold starts causing latency spikes. You want to minimize cold starts cost-effectively. What should you do?

Accepted Answer

Set min-instances to 1 to keep at least one instance always warm.. Setting min-instances to 1 ensures that at least one instance of your Cloud Run service is always kept warm, meaning it is initialized and ready to handle requests immediately. This eliminates cold starts for the first request after a period of inactivity, reducing latency spikes without requiring over-provisioning of resources. It is cost-effective because you only pay for the idle instance when it is not serving traffic, and you avoid the higher costs of larger machine types or excessive concurrent instances.

Answer

Increase the max-instances setting to allow more concurrent requests.

Answer

Set concurrency to 1 to ensure each instance handles one request at a time.

Answer

Use a larger machine type (e.g., 2 vCPU) to reduce startup time.

Question 3

A company runs batch processing jobs on a GKE cluster using preemptible node pools. The jobs are fault-tolerant and can be interrupted. However, the cluster is experiencing high costs due to underutilized nodes. The batch jobs run for 2-3 hours each. What is the most cost-effective optimization?

Accepted Answer

Create multiple node pools with different machine types and use node auto-provisioning with preemptible nodes and custom machine types.. The most cost-effective because it leverages node auto-provisioning with preemptible nodes and custom machine types, which dynamically creates node pools tailored to the specific resource requirements of each batch job. This eliminates waste from over-provisioned nodes while maintaining fault tolerance for interruptible workloads. Combined with preemptible instances (up to 60-80% cheaper than regular VMs), this approach minimizes cost without sacrificing job completion, as the jobs are already designed to handle interruptions.

Answer

Switch to compute-optimized (C2) machine types for faster job completion.

Answer

Use regional persistent disks for stateful workloads to improve performance.

Answer

Reduce the number of min-nodes in the node pool to zero during idle times and use cluster autoscaler.

Question 4

A company has an on-premises data center connected to GCP via Dedicated Interconnect. They run latency-sensitive applications on GCE and use Cloud Storage for backups. The backup traffic is causing congestion on the Interconnect link. How should they optimize costs and performance?

Accepted Answer

Route backup traffic through a separate VPN tunnel over the internet to reduce congestion on the Interconnect.. Routing backup traffic over a separate VPN tunnel using the internet offloads non-latency-sensitive backup data from the Dedicated Interconnect link, reducing congestion without incurring additional costs for increased bandwidth. This approach preserves the low-latency path for critical application traffic while using a cost-effective, encrypted internet-based connection for backups, optimizing both performance and cost.

Answer

Increase the Dedicated Interconnect bandwidth to accommodate both traffic types.

Answer

Move backup storage to a different region to reduce data transfer costs.

Answer

Use Cloud Interconnect to connect directly to Cloud Storage for backup traffic.

Question 5

A company uses BigQuery for analytics and has a large number of ad-hoc queries from different teams. Costs are rising unpredictably. They want to control costs while maintaining query performance. What should they do?

Accepted Answer

Purchase flat-rate slots and assign them to a reservation for each team.. Purchasing flat-rate slots and assigning them to a reservation for each team provides predictable, fixed-cost capacity for BigQuery. This eliminates the unpredictability of on-demand pricing while allowing teams to share a dedicated pool of slots, ensuring consistent query performance without unexpected cost spikes.

Answer

Use partitioning and clustering to reduce data scanned.

Answer

Reduce the number of slots available to each team.

Answer

Require each team to include a cost code in their queries.

Question 6

A financial services company needs a disaster recovery plan for a critical application running on GKE. The application uses Cloud SQL for MySQL. The RPO is 5 minutes and RTO is 15 minutes. Which design meets these requirements cost-effectively?

Accepted Answer

Use Cloud SQL cross-region replication (MySQL) with a failover replica in another region, and deploy GKE cluster in that region with the same configuration.. Cloud SQL cross-region replication provides an asynchronous replica in another region with a typical replication lag of a few seconds, meeting the 5-minute RPO. The failover replica can be promoted in minutes, and deploying a GKE cluster in the same region with identical configuration allows the application to connect to the promoted replica, achieving the 15-minute RTO. This approach is cost-effective because you only pay for the replica and the second GKE cluster when needed for failover testing or actual disaster recovery.

Answer

Use Cloud SQL for MySQL with multi-region database flag and route traffic to nearest region via Cloud Load Balancing.

Answer

Set up VPC peering between two regions and use Cloud DNS to direct traffic in failover.

Answer

Use Cloud SQL backups to Cloud Storage and restore in another region, with GKE cluster recreated via Deployment Manager.

Question 7

A company uses Cloud Build to automate their CI/CD pipeline. They want to optimize the build process for a Java application. Which three practices should they adopt? (Choose three.)

Accepted Answer

Parallelize independent build steps by using Cloud Build's step parallelism or by splitting into multiple builds.. Option A is correct because Cloud Build executes steps sequentially by default, and parallelizing independent steps (e.g., running unit tests and static analysis concurrently, or splitting work across multiple builds) reduces total wall-clock build time. Option B is correct because storing Maven dependencies in a private Artifact Registry repository lets Cloud Build resolve artifacts from a low-latency, co-located Google Cloud service instead of repeatedly pulling from public Maven Central, speeding up dependency resolution and improving reliability. Option C is correct because specifying a cached image (e.g., via the --cache-from flag or a Kaniko cache) enables Docker layer caching so unchanged layers are reused rather than rebuilt, which is a standard Cloud Build optimization for container-based Java builds. Option D is not appropriate because downloading all tools from the internet on every build adds latency, introduces external dependencies, and is the opposite of an optimization; tools should be baked into builder images or cached. Option E is not appropriate because simply choosing a high-CPU machine type increases cost without addressing the structural inefficiencies (sequential steps, uncached dependencies and layers) that dominate build time, and it is not one of the recommended optimization practices here.

Answer

Use a custom build step that downloads all tools from the internet each time.

Answer

Use a high-CPU machine type (e.g., n1-highcpu-64) for faster compilation.

Question 8

A team deployed a new version of a service on Cloud Run. After deployment, users report 502 errors. The logs show "Error: Server Error" with no stack trace. What is the first step the team should take to diagnose the issue?

Accepted Answer

Check the container image for missing dependencies or misconfiguration.. The 502 error with 'Error: Server Error' and no stack trace typically indicates that the container failed to start or crashed immediately after startup, often due to missing dependencies or misconfiguration in the container image. Cloud Run requires the container to listen on the port specified by the PORT environment variable (default 8080) and respond to health checks; if the container exits or fails to bind, Cloud Run returns a 502. Checking the container image is the first diagnostic step because it addresses the most common root cause before investigating runtime or scaling issues.

Answer

Enable Cloud Debugger to inspect the running instance.

Answer

Increase Cloud Run max instances limit.

Answer

Rollback to the previous version immediately.

Question 9

A company wants to implement a CI/CD pipeline for a microservices application on GKE. They require automated canary deployments with gradual traffic shifting and automatic rollback on metric failure. Which Google Cloud service is most suitable?

Accepted Answer

Cloud Deploy with Skaffold.. Cloud Deploy with Skaffold is the most suitable because it provides native support for progressive delivery on GKE, including automated canary deployments with gradual traffic shifting (using Service Mesh or Ingress) and automatic rollback based on Cloud Monitoring metrics. Skaffold handles the build and deploy configuration, while Cloud Deploy manages the rollout pipeline, approval gates, and metric-driven rollback logic without requiring manual intervention.

Answer

Cloud Build with Deployment Manager.

Answer

Spinnaker on GKE.

Answer

Istio with manual traffic management.

Question 10

A financial services company uses Cloud SQL for MySQL for a critical application. They need zero downtime during maintenance and automatic failover across zones. They configured a Cloud SQL instance with high availability (HA). During a recent regional outage, the application experienced 10 minutes of downtime. What should they add to improve availability?

Accepted Answer

Create a cross-region replica and promote on failure.. A cross-region replica provides a read-replica in a different region that can be promoted to a primary instance during a regional outage, enabling recovery with minimal downtime. This addresses the scenario where a single-region HA configuration (which uses zonal redundancy within the same region) cannot survive a full regional outage, as occurred in the question. Promoting the replica is a manual or automated failover action that restores write capability in the secondary region, reducing downtime from 10 minutes to seconds or minutes depending on replication lag.

Answer

Configure a warm standby instance in another region using migration.

Answer

Use Cloud SQL Proxy with multiple endpoints.

Answer

Enable database flags for faster failover detection.

Question 11

A company is using BigQuery for data analytics. They want to optimize costs while maintaining query performance. Which TWO actions should they take? (Choose 2.)

Accepted Answer

Partition tables by date.. Option C is correct because partitioning a BigQuery table by date (e.g., a DATE or TIMESTAMP column) lets queries use partition pruning, so the engine scans only the relevant partitions instead of the whole table, directly reducing bytes billed and cost while keeping performance high. Option E is correct because clustering on frequently filtered columns physically co-locates related rows, so filters and aggregations on those columns read far less data, again lowering cost without sacrificing query speed. Option A is not appropriate here because reserved slots with flat-rate pricing are a capacity commitment model that can reduce cost only at predictable, high, sustained usage; it does not by itself optimize per-query scan costs and may be more expensive for variable workloads. Option B is wrong because SELECT * forces BigQuery to read every column, maximizing bytes scanned and cost, which is the opposite of optimization. Option D is not among the marked answers and, while materializing results can help, it introduces storage cost and staleness concerns and is not one of the two actions selected as correct for this scenario.

Answer

Use reserved slots with flat-rate pricing.

Answer

Always use SELECT *.

Answer

Materialize frequently used queries as tables.

Question 12

A company is migrating a legacy monolithic application to Google Cloud. They want to adopt microservices and improve deployment frequency. Which THREE practices should they adopt? (Choose 3.)

Accepted Answer

Use Infrastructure as Code (IaC) with Terraform.. Option A is correct because Infrastructure as Code with Terraform lets the team provision and version GCP resources (VPCs, GKE clusters, Cloud Run services, IAM) declaratively, making environments reproducible and enabling faster, safer microservice deployments. Option D is correct because canary releases with traffic splitting (e.g., GKE/Anthos or Cloud Run traffic splitting, or App Engine versions) let you route a small percentage of production traffic to a new service version and progressively shift it, reducing deployment risk and supporting higher release frequency. Option E is correct because immutable infrastructure principles mean servers/containers are never patched in place but replaced with newly built images, which pairs naturally with microservices, containerization, and automated pipelines to make rollbacks deterministic and deployments repeatable. Option B is not appropriate because a single pipeline for all services creates a monolithic release bottleneck and couples independent microservices, undermining the goal of faster, independent deployments. Option C is not selected because blue/green deployments are a valid release strategy but are not among the three practices marked correct here; the scenario's emphasis on progressive, low-risk traffic shifting is better served by canary releases with traffic splitting.

Answer

Build a single deployment pipeline for all services.

Answer

Implement blue/green deployments.

Question 13

A company runs a batch processing workload on Compute Engine. They need to minimize cost and ensure jobs complete within a 24-hour window. Which THREE strategies should they implement? (Choose 3.)

Accepted Answer

Use preemptible VMs for fault-tolerant jobs.. Option B is correct because preemptible VMs cost up to 80% less than standard VMs and are ideal for fault-tolerant batch jobs that can be checkpointed and restarted within a 24-hour window. Option D is correct because committed use discounts (CUDs) provide significant savings (up to 57% for 1-year terms) on predictable, steady-state Compute Engine usage, which suits a recurring batch workload. Option E is correct because a managed instance group with autoscaling based on job queue depth dynamically matches capacity to workload demand, avoiding over-provisioning costs while ensuring jobs finish within the deadline. Option A is incorrect because sole-tenant nodes are for physical isolation and compliance, not cost minimization. Option C is incorrect because instance reservations guarantee capacity but do not reduce cost and are unnecessary for a batch workload that can tolerate preemption.

Answer

Use sole-tenant nodes for resource isolation.

Answer

Configure instance reservations for guaranteed capacity.

Question 14

A company runs a customer-facing web application on Google Kubernetes Engine (GKE) in us-central1. The application uses a Cloud SQL for PostgreSQL database for user data. Recently, they noticed that during peak hours, the application response times increase significantly, and some requests time out. The team has already scaled the GKE nodepool to the maximum size, but the issue persists. Database CPU utilization is at 80%, and connections are near the max limit. The application uses connection pooling via PgBouncer running as a sidecar. The team suspects the database is the bottleneck. They need to improve performance with minimal cost impact. What should they do?

Accepted Answer

Add read replicas and split read/write traffic.. Adding read replicas and splitting read/write traffic offloads read queries from the primary Cloud SQL instance, reducing CPU and connection pressure. PgBouncer as a sidecar can be configured to route read-only transactions to replicas, while writes go to the primary. This directly addresses the 80% CPU and max connections issue without increasing costs as much as scaling up the machine type.

Answer

Enable Cloud SQL automatic storage increase.

Answer

Increase the max connections parameter on Cloud SQL.

Answer

Increase the Cloud SQL machine type to the next tier.

Question 15

A company is using Cloud Functions (2nd gen) for event-driven processing of uploaded images in Cloud Storage. Each image is resized to multiple sizes and stored back in different buckets. Recently, the number of uploads has increased 10x, and the team notices that some images are not being processed, and logs show function execution timeouts after 60 seconds. The function's timeout is set to 60 seconds. The code processes images sequentially. The team needs to reliably process all images with minimal code changes. What should they do?

Accepted Answer

Use Cloud Tasks to queue the processing and run the function as a task handler.. Using Cloud Tasks to queue image processing decouples the upload event from the processing function, allowing asynchronous execution with retries and longer processing times. The function can be invoked as a task handler, and Cloud Tasks manages the queue, ensuring reliable delivery even under high load. This requires minimal code changes: the function's entry point is modified to handle task requests, and the upload event triggers task creation instead of direct function invocation.

Answer

Increase the memory allocation for the function.

Answer

Increase the Cloud Function timeout to 540 seconds.

Answer

Split the function into separate functions for each resize operation.