PCD Building and testing applications — 10 Questions

Question 1

A developer is designing a CI/CD pipeline for a Node.js application hosted on Cloud Run using Cloud Build. The pipeline should run unit tests, build the container, push to Artifact Registry, and deploy to Cloud Run. The developer wants to minimize build time by caching dependencies. What is the recommended approach?

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

Accepted Answer

Use Cloud Build's built-in caching with a persistent volume to store node_modules between builds.. Cloud Build supports built-in caching via persistent volumes (e.g., `/cache` or `/workspace`) that can store `node_modules` across builds. By configuring a cache volume in the `cloudbuild.yaml` and using `npm ci --prefer-offline`, the pipeline avoids re-downloading dependencies on every run, significantly reducing build time for Node.js applications on Cloud Run.

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Run npm install locally and commit the node_modules folder to the repository for faster builds.

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Use Cloud Build's step-level caching by copying the node_modules from a previous build step.

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Create a custom base image that includes all dependencies and reference it in the Dockerfile.

Question 2

A team is building a mobile backend on Google Cloud using Cloud Endpoints with Firebase Authentication. They want to protect their API from abuse by implementing rate limiting per user. What approach should they take?

Accepted Answer

Use Apigee API Management as a proxy to enforce rate limiting per developer app.. Apigee API Management is the correct choice because it provides built-in rate limiting policies that can be enforced per developer app, which maps directly to per-user rate limiting when Firebase Authentication is used. Cloud Endpoints does not natively support per-user rate limiting; it relies on the backend to implement such logic, which is not a managed solution. Apigee acts as a proxy that can inspect the Firebase-issued JWT token to identify the user and apply rate limits accordingly, offloading this concern from the backend code.

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Implement rate limiting in the backend code and enforce it via Cloud Endpoints.

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Configure Cloud Armor with a rule to block requests from users exceeding a threshold.

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Use Cloud CDN with a cache key based on the user ID.

Question 3

Which TWO statements about building container images for Google Cloud Run are correct? (Choose TWO.)

Accepted Answer

The container must respond to health checks on the same port as the main application.. Option B is correct because Cloud Run sends startup and liveness probes to the same container port that serves traffic (the port set via the PORT environment variable, default 8080), so the health-check endpoint must be exposed on that port. Option C is correct because Cloud Run instances are ephemeral and can be scaled down or replaced at any time, so any state written to the local filesystem is lost; persistent data must go to external services like Cloud Storage, Firestore, or Cloud SQL. Option A is wrong because Cloud Run supports HTTP/2 (including end-to-end HTTP/2 and gRPC) when configured, so HTTP/1.1 is not the only allowed protocol. Option D is wrong because although 8080 is the default, Cloud Run injects a PORT environment variable and the container may listen on any port it configures, so 8080 is not mandatory. Option E is wrong because Cloud Run does not require running as root and in fact best practice is to run as a non-root user.

Answer

The container must use HTTP/1.1; HTTP/2 is not supported.

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The container must listen on port 8080 by default.

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The container must run as root user.

Question 4

A team is developing a microservice that processes messages from Pub/Sub. The service is deployed on Cloud Run and uses Cloud Firestore to store processed data. During load testing, the service frequently fails with 'DeadlineExceeded' errors from Firestore. What is the most likely cause and best practice to fix it?

Accepted Answer

Increase the Cloud Run container instance request timeout. The 'DeadlineExceeded' error is raised by the Firestore client-side SDK when the operation exceeds its default timeout (e.g., 60 seconds). However, the underlying cause in this Cloud Run environment is that the default Cloud Run request timeout (5 minutes) is too short for the entire processing pipeline, causing the container to be terminated before the Firestore operation completes. When the container is terminated, the pending Firestore request fails with 'DeadlineExceeded'. Increasing the Cloud Run request timeout prevents premature termination, allowing the Firestore operation to finish within the container's lifetime. This is the most direct fix, as it addresses the infrastructure timeout that triggers the client-side error.

Answer

Increase the Pub/Sub subscription acknowledgment deadline

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Enable CPU always allocation for the Cloud Run service

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Add retry logic with exponential backoff for Firestore operations

Question 5

Which TWO practices should be followed when integrating Cloud Endpoints with a Cloud Run service to enforce API authentication and rate limiting?

Accepted Answer

Set the audience field in the Endpoints service configuration to the Cloud Run service URL. Option B is correct because when Cloud Endpoints (ESPv2) validates a JWT for a Cloud Run backend, the audience in the Endpoints service configuration must match the Cloud Run service URL so the token's aud claim is validated against the intended backend. Option C is correct because rate limiting (quota) in Cloud Endpoints is declared in the OpenAPI specification using the x-google-quota extension properties, which the ESPv2 proxy enforces per API key or consumer. Option A is not appropriate because API keys identify projects/apps rather than authenticating end users, and the scenario calls for API authentication. Option D is wrong because Cloud Endpoints is deployed as a separate proxy (ESPv2) in front of Cloud Run, not as a sidecar container inside the same Cloud Run instance. Option E is wrong because Cloud Armor provides edge security (WAF/DDoS) and does not implement Endpoints API-level quota/rate limiting defined in the OpenAPI spec.

Answer

Use API keys to authenticate end users

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Deploy Cloud Endpoints as a sidecar container in the same Cloud Run instance

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Configure Cloud Armor rules to enforce rate limiting before requests reach Endpoints

Question 6

Which THREE steps are required to set up end-to-end testing for a Cloud Run service that uses Firestore and Pub/Sub?

Accepted Answer

Automate the teardown of test resources after test completion. Option E is correct because creating a separate Google Cloud project for testing provides full isolation of IAM, quotas, billing, and resources, ensuring end-to-end tests never touch production data or services. Option C is correct because Pub/Sub topics and subscriptions are project-scoped resources; provisioning dedicated test topics and subscriptions guarantees the Cloud Run service under test publishes and consumes messages in an isolated channel rather than interfering with production message flows. Option A is correct because end-to-end tests that provision real cloud resources (project, topics, subscriptions) must automate teardown afterward to avoid orphaned resources, quota consumption, and recurring costs. Option B is not required because the Cloud Run emulator only runs the container locally and cannot exercise real Firestore or Pub/Sub integrations needed for true end-to-end testing. Option D is not required because the Firestore emulator only simulates Firestore locally and would not validate the real managed-service behavior that end-to-end testing targets.

Answer

Use the Cloud Run emulator to run the service locally

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Use the Firestore emulator to simulate Firestore operations

Question 7

Refer to the exhibit. A developer is configuring Cloud Build to build a Docker image from a Cloud Source Repository. The build fails with a permission error. What is the most likely reason?

Accepted Answer

The service account is missing the roles/source.reader role to access the repository. The build fails with a permission error because the Cloud Build service account does not have the `roles/source.reader` role on the Cloud Source Repository. Without this role, the service account cannot read the source code from the repository, which is required to trigger the build. The error is not about building permissions but about accessing the source.

Answer

The service account lacks the roles/cloudbuild.builds.builder role

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The Cloud Source Repository does not have the build trigger enabled

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The build config file is missing the 'source' field

Question 8

A developer is deploying a new version of a microservice to Cloud Run. The developer wants to ensure that the new revision is tested with a small percentage of traffic before rolling out to all users. Which TWO approaches can the developer use?

Accepted Answer

Use the 'gcloud run deploy' command with '--no-traffic' and then use 'gcloud run services update-traffic --to-revisions=REVISION=5' to send 5% of traffic.. Option A is correct because deploying with '--no-traffic' creates the new revision without receiving any production traffic, and then 'gcloud run services update-traffic --to-revisions=REVISION=5' explicitly assigns exactly 5% of traffic to that revision, enabling a small-percentage canary test. Option B is correct because the same '--no-traffic' deployment followed by 'gcloud run services update-traffic' allows the developer to progressively shift traffic to the new revision in controlled increments, which is the standard Cloud Run gradual rollout pattern. Option C is incorrect because 'max-instances' only caps the number of container instances for scaling and does not control the percentage of requests routed to a revision. Option D is incorrect because '--tag' creates a tagged URL for direct access to a revision (useful for testing by URL), but it does not split production traffic by percentage. Option E is incorrect because Cloud Run revisions are immutable and uniquely named; you cannot overwrite an existing revision by reusing its name, and this approach would not provide percentage-based traffic testing.

Answer

Set the 'max-instances' parameter to limit the number of instances handling requests.

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Use the 'gcloud run deploy' command with '--tag' to assign a tag to the new revision, then direct test traffic to that tag.

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Deploy the new revision with the same revision name as the old one to overwrite it, then roll back if issues occur.

Question 9

You are a developer for an e-commerce platform running on Google Kubernetes Engine (GKE) with a Cloud SQL backend. The application uses Cloud Memorystore for Redis for session caching. During a flash sale, you notice that the application latency spikes and some users are unable to complete checkout. You suspect the Redis instance is overwhelmed. The Redis instance is currently a Standard tier instance with 5 GB of memory. You need to increase throughput without significant architectural changes. You have the following options: A) Use client-side caching to reduce load on the Redis instance. B) Switch to a Memorystore Standard tier instance with a higher capacity and enable scaling. C) Migrate to a Memorystore Basic tier instance with a larger memory size. D) Enable for Redis clustering on the existing instance to distribute load across shards. Which option should you choose?

Accepted Answer

Switch to a Memorystore Standard tier instance with a higher capacity and enable scaling.. Enabling scaling on a Memorystore Standard tier instance allows you to increase the instance's capacity and throughput without architectural changes. Scaling up the memory size increases the available CPU and network bandwidth, directly addressing the latency spike during the flash sale. This approach maintains the existing Redis configuration and requires no application code changes, unlike clustering or client-side caching.

Answer

Use client-side caching to reduce load on the Redis instance.

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Migrate to a Memorystore Basic tier instance with a larger memory size.

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Enable for Redis clustering on the existing instance to distribute load across shards.

Question 10

Your team has developed a containerized application that processes streaming data from Pub/Sub. The application is deployed on Cloud Run. Under normal load, it processes messages within seconds. However, during spikes, processing time increases and some messages are not acknowledged before the Cloud Run request timeout of 60 minutes. You need to ensure that all messages are processed reliably without losing data. You have the following options: A) Increase the Cloud Run request timeout to 120 minutes. B) Use Cloud Run jobs instead of services to handle the processing asynchronously. C) Set up a second subscription to Pub/Sub with a different push endpoint to parallelize processing. D) Use a Cloud Tasks queue to decouple the Pub/Sub push and process messages with retries. Which option should you choose?

Accepted Answer

Use Cloud Run jobs instead of services to handle the processing asynchronously.. Cloud Run jobs are designed for asynchronous, batch-style processing that can run longer than the 60-minute request timeout of Cloud Run services. By using a job, you can pull messages from Pub/Sub, process them without a hard timeout, and acknowledge them only after successful processing, ensuring reliable message handling during spikes.

Answer

Use a Cloud Tasks queue to decouple the Pub/Sub push and process messages with retries.

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Increase the Cloud Run request timeout to 120 minutes.

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Set up a second subscription to Pub/Sub with a different push endpoint to parallelize processing.