PCD Practice Test 16 — 15 Questions

Question 1

A company is building a microservice that processes incoming HTTP requests, performs some business logic, and writes results to Firestore. The service has variable traffic with occasional spikes. The development team wants to minimize cold start latency and prefers to use a containerized application with a custom runtime. Which compute option should they choose?

Accepted Answer

Cloud Run. Cloud Run is the correct choice because it runs containerized applications in a fully managed, serverless environment that automatically scales to zero and can handle variable traffic with occasional spikes. It minimizes cold start latency by keeping instances warm when traffic is expected, and it supports custom runtimes via Docker containers, meeting the team's requirement for a containerized application with a custom runtime.

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Compute Engine

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App Engine Standard

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Cloud Functions (1st gen)

Question 2

A company needs to build a CI/CD pipeline for a microservices architecture. They want to run unit tests quickly by only testing code that has changed. Which approach should they use?

Accepted Answer

Use Cloud Build with a step that checks `git diff` against the previous commit and runs tests only on affected modules using a test runner that supports file-based filtering.. Option D is correct because using a custom builder to check the diff and run only relevant tests is a best practice for fast CI. Option A is suboptimal because running all tests in parallel is not selective. Option B is not a built-in feature and can be unreliable. Option C is not as efficient as a custom diff-based approach.

Answer

Use Cloud Build with a step that caches test results based on file hashes.

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Use Cloud Build with a step that runs all tests in parallel.

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Use Cloud Build with a step that uses `git log` to find changed files and run tests.

Question 3

A company is using Cloud Deploy to manage canary deployments to GKE. They want to automatically promote a release to the 'production' target if the canary deployment in the 'staging' target passes a set of automated smoke tests. What is the required configuration?

Accepted Answer

Define a deployment verifier in the pipeline that runs smoke tests and promotes on success.. Option B is correct because Cloud Deploy supports deployment verifiers, which are custom Cloud Build jobs that run as part of a rollout. By defining a verifier in the pipeline that executes automated smoke tests, the canary deployment in the staging target can be automatically promoted to production only if the verifier succeeds. This integrates testing directly into the delivery pipeline without manual intervention.

Answer

Create a Cloud Build trigger to redeploy on test success.

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Configure a manual approval gate between staging and production in the delivery pipeline.

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Set the 'automaticPromotion' flag to true on the staging target.

Question 4

A team is setting up a CI/CD pipeline using Cloud Build for a Node.js application. They want to ensure that only code from the main branch is deployed to production. Which TWO practices should they implement?

Accepted Answer

Use branch triggers to run tests only on push to main.. Using a branch trigger that runs only on push to main ensures that only main branch code triggers the pipeline. Adding a manual approval step in Cloud Deploy before promoting to production adds a gate to prevent automatic deployment of untested code. Storing secrets or using substitutions are good practices but do not specifically restrict deployment to the main branch.

Answer

Store secrets in Cloud Build and use them in build steps.

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Use Cloud Build substitutions to inject environment variables.

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Use Cloud Build's inverted match with branch pattern to exclude non-main branches.

Question 5

Refer to the exhibit. The developer receives an error when creating the delivery pipeline. What is the most likely cause?

Accepted Answer

The canary percentages for the prod target do not sum to 100.. Option C is correct because the sum of the percentages in the prod stage is 10+20+60=90, and the last value 100 is not an increment but the final full rollout. The increments must sum to 100. Option A is incorrect because four percentages are allowed. Option B is not a requirement for pipeline creation. Option D is unlikely.

Answer

The prod target is missing a verification step.

Answer

The dev target has four percentages, but only two are allowed.

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The pipeline name is too long.

Question 6

A developer is designing a data pipeline using Pub/Sub and Dataflow. They need to guarantee at-least-once delivery with no duplicates in the sink. Which Dataflow feature should they use?

Accepted Answer

Exactly-once processing. Option A is correct because Dataflow's exactly-once processing (also known as 'exactly-once semantics' or 'EOS') ensures that each record is processed exactly once, even if the pipeline restarts or fails. This eliminates duplicates in the sink while still guaranteeing at-least-once delivery from Pub/Sub, because Dataflow uses a combination of source-side deduplication (via Pub/Sub message IDs) and sink-side idempotent writes (via the Dataflow sink's commit protocol). The result is that no duplicate records are written to the sink, meeting the requirement of no duplicates.

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Checkpointing

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Idempotent writes

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Windowing

Question 7

A team uses Google Kubernetes Engine (GKE) with Node Auto-Provisioning. They want to optimize cost while maintaining high availability across zones. Which two strategies should they implement? (Select exactly 2.)

Accepted Answer

Use cluster autoscaler with appropriate min and max node counts. Option A is correct because Node Auto-Provisioning (NAP) in GKE works in conjunction with the cluster autoscaler to automatically create and delete node pools based on workload demands. By setting appropriate minimum and maximum node counts, you ensure the cluster can scale down to zero when idle (saving cost) and scale up to handle peak load, while avoiding runaway scaling that could increase costs unexpectedly.

Answer

Use preemptible VMs for all node pools

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Disable cluster autoscaler to prevent scaling

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Use sole-tenant nodes for high availability

Question 8

A company wants to design a highly available web application that serves users globally. They plan to use Cloud Load Balancing. Which two design choices should they make to ensure high availability and low latency? (Choose two.)

Accepted Answer

Enable Cloud CDN to cache static content closer to users.. Enabling Cloud CDN caches static content at Google's global edge locations, reducing latency by serving content from a point of presence (PoP) close to the user. This offloads requests from backend instances, improving overall availability and performance for global users.

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Use a single-zone backend instance group for simplicity.

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Use Cloud Armor to filter malicious traffic.

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Deploy separate regional load balancers in each region and use DNS-based routing.

Question 9

Which three factors should be considered when choosing a regional vs. multi-regional deployment for a globally distributed application?

Accepted Answer

Data residency requirements. A is correct because data residency requirements mandate that certain data must remain within specific geographic boundaries due to legal or regulatory obligations (e.g., GDPR, HIPAA). Choosing a regional deployment ensures data stays within a single region, while multi-regional deployment may require complex data replication and compliance with multiple jurisdictions. This directly impacts architectural decisions for globally distributed applications.

Answer

Single region compliance

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Replication lag

Question 10

A company is designing a cloud-native application on Google Kubernetes Engine. They want to ensure high availability and scalability for their microservices. Which two best practices should they follow?

Accepted Answer

Use horizontal pod autoscaling based on custom metrics.. Horizontal Pod Autoscaling (HPA) based on custom metrics allows the application to automatically scale the number of pod replicas in response to application-specific signals (e.g., requests per second, queue depth) rather than just CPU/memory. This ensures that each microservice can handle varying load efficiently, maintaining high availability and scalability without over-provisioning.

Answer

Use a single cluster per region.

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Use a single replica for each service to reduce cost.

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Use stateful sets for all services.

Question 11

The developer runs the command above and sees both instances are unhealthy. The instances are running and serving traffic on port 80 when accessed directly. What is the most likely cause?

Accepted Answer

Firewall rules block the health check probe IP ranges. The most likely cause is that firewall rules are blocking the health check probe IP ranges. Google Cloud Platform (GCP) load balancers use specific, documented IP ranges for health check probes. If a firewall rule denies traffic from these ranges, the load balancer will mark the instances as unhealthy even though the instances are running and serving traffic on port 80 when accessed directly. This is a common misconfiguration because the health check probes originate from these special IP ranges, not from the load balancer's frontend IP.

Answer

The instances have been deleted

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The instances are not running the specified health check port

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The load balancer is misconfigured

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The instances are out of memory and unable to respond

Question 12

A financial trading application on Compute Engine requires an RPO of 5 seconds and RTO of 1 minute for zone failures. Which architecture should they use?

Accepted Answer

Regional persistent disk attached to a single instance. Regional persistent disks provide synchronous replication of data between two zones within a region, ensuring an RPO of effectively zero (typically under 5 seconds) and enabling rapid failover to a secondary zone. By attaching the regional disk to a single Compute Engine instance, the application can quickly resume operations in the other zone upon failure, meeting the 1-minute RTO without data loss or complex replication overhead.

Answer

Persistent disk with periodic snapshots to a different zone

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Managed instance group with autoscaling and health checks

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Two instances in different zones with data replicated via rsync

Question 13

A company uses Cloud Storage for backups. They need to comply with a regulation requiring immutable storage for 7 years. Which bucket configuration should they use?

Accepted Answer

Set a retention policy and lock the bucket. Option D is correct because locking a retention policy in Cloud Storage enforces immutable storage for the specified duration (7 years). Once locked, the retention policy cannot be removed or shortened, ensuring compliance with regulations that require data to be preserved in its original state and not modifiable or deletable until the retention period expires.

Answer

Use a bucket with a retention policy (not locked)

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Set a lifecycle rule to archive to Coldline

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Enable Object Versioning

Question 14

A startup is deploying a stateless web app on Compute Engine. They expect traffic spikes. What is the most cost-effective way to handle scaling?

Accepted Answer

Use managed instance groups with autoscaling based on CPU utilization.. Managed instance groups (MIGs) with autoscaling based on CPU utilization are the most cost-effective solution for a stateless web app with traffic spikes because they automatically add or remove VM instances in response to real-time CPU load, ensuring you only pay for the compute resources you actually use. This approach directly matches the stateless nature of the app, allowing instances to be created and destroyed without data loss, and avoids over-provisioning or under-utilizing resources.

Answer

Use App Engine Standard.

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Use a single large VM with more cores.

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Use Cloud Functions.

Question 15

Refer to the exhibit. A company configured an HPA for their deployment. They notice that the HPA is not scaling based on the 'packets-per-second' metric. What is the most likely reason?

Accepted Answer

The metric is not available in the cluster.. The 'packets-per-second' metric is a custom metric. If it is not registered in the cluster's metrics server (e.g., via custom metrics adapter), the HPA will not be able to collect it. The metric name and target type are correct. The scaleTargetRef matches the deployment. Therefore, the metric being unavailable is the most likely issue.

Answer

The metric name 'packets-per-second' is incorrect.

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The target type should be 'Value' instead of 'AverageValue'.

Answer

The HPA is using the wrong scaleTargetRef.