PCD Building and testing applications Practice Test 7 — 15 Questions

Question 1

A developer is building a CI/CD pipeline for a microservices application. The pipeline should build a container image, run unit tests, and deploy to Google Kubernetes Engine (GKE) only if all tests pass. Which Google Cloud service is best suited for orchestrating this pipeline?

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

Accepted Answer

Cloud Build. Cloud Build is the correct choice because it is a fully managed CI/CD platform that natively supports building container images, running unit tests, and deploying to GKE. It can be configured with a cloudbuild.yaml file to define steps for building, testing, and deploying, and it only proceeds to the deploy step if all prior steps (including tests) succeed. This makes it the best fit for orchestrating the entire pipeline in a single, integrated service.

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Compute Engine

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Cloud Run

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Cloud Functions

Question 2

A team uses Cloud Source Repositories for version control and Cloud Build for CI. The build configuration file (cloudbuild.yaml) includes a step that runs unit tests. The team wants to ensure that the build fails if any test fails. What should the developer do?

Accepted Answer

Ensure the test command in the build step returns a non-zero exit code when tests fail.. Cloud Build executes each step as a container, and the build step's success or failure is determined by the exit code of the command run inside that container. If the test command (e.g., `npm test` or `pytest`) returns a non-zero exit code when tests fail, Cloud Build will automatically mark that step as failed and stop the build. No special configuration or custom builder is required beyond ensuring the test command itself propagates the failure exit code.

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Use Cloud Build's built-in test runner that automatically fails the build on test failure.

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Create a custom builder that runs tests and emits a non-zero exit code on failure.

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Add a pre-build step that checks test results and triggers a build failure if needed.

Question 3

An application running on Compute Engine uses Cloud Storage for storing user-uploaded images. During load testing, the application experiences high latency when reading images. The developer suspects that the application is making too many small read requests. Which approach should the developer take to optimize performance?

Accepted Answer

Increase the read size to reduce the number of API requests.. The high latency is caused by many small read requests, each incurring API overhead. By increasing the read size (e.g., reading larger chunks or using range requests), the application reduces the number of API calls, which lowers cumulative latency and improves throughput. This directly addresses the root cause of excessive small reads.

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Enable Cloud CDN to cache the images at edge locations.

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Rewrite the objects to use a different storage class.

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Mount the Cloud Storage bucket using Cloud Storage FUSE and read files from the local filesystem.

Question 4

A developer runs the above command and cloudbuild.yaml. The build fails at the deploy step with a permission error. The developer has the Cloud Build Editor role on the project. What is the likely cause?

Accepted Answer

The Cloud Build service account lacks the Cloud Run Admin role.. The Cloud Build Editor role grants permissions to submit builds and execute build steps, but the actual execution of those steps (including the deploy step) runs under the Cloud Build service account. By default, this service account does not have the Cloud Run Admin role, which is required to deploy to Cloud Run. Without this role, the `gcloud run deploy` command fails with a permission error.

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The Cloud Build Editor role does not have permission to submit builds.

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The Docker image is not in a format compatible with Cloud Run.

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The build step uses the 'gcloud' command without authentication.

Question 5

Refer to the exhibit. A Cloud Build config deploys a new image to GKE. After the build succeeds, the pods restart with the new image but the application configuration is unchanged. What is the most likely cause?

Accepted Answer

The ConfigMap is not updated with the new configuration values.. A is correct because a Cloud Build config that deploys a new image to GKE does not automatically update the ConfigMap. The pods restart with the new image, but the application configuration remains unchanged because the ConfigMap still holds the old values. To apply new configuration, the ConfigMap must be updated separately, and the pods must be restarted or redeployed to pick up the changes.

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The deployment rollout strategy is set to Recreate, causing downtime.

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The new image is not being pulled because of imagePullPolicy: IfNotPresent.

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The GKE cluster does not have sufficient permissions to pull from Container Registry.

Question 6

Drag and drop the steps to set up a Cloud Build trigger for continuous deployment in the correct order.

Accepted Answer

1. Connect your repository (e.g., GitHub, Cloud Source Repositories). 2. Create a new trigger in Cloud Build. 3. Configure the trigger event (e.g., push to a specific branch). 4. Specify the build configuration file (e.g., cloudbuild.yaml).. A Cloud Build trigger is set up by connecting a repository and configuring the conditions for automatic builds.

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1. Create a new trigger in Cloud Build. 2. Connect your repository. 3. Configure the trigger event. 4. Specify the build configuration file.

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1. Configure the trigger event. 2. Connect your repository. 3. Create a new trigger in Cloud Build. 4. Specify the build configuration file.

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1. Specify the build configuration file. 2. Connect your repository. 3. Create a new trigger in Cloud Build. 4. Configure the trigger event.

Question 7

Drag and drop the steps to configure a Cloud CDN with a Cloud Load Balancer in the correct order.

Accepted Answer

Create a Cloud Storage bucket, then create an external HTTP(S) load balancer with the bucket as a backend, then enable Cloud CDN on the backend bucket, then update DNS records to point to the load balancer's IP.. Cloud CDN is enabled on a backend bucket of a load balancer, then DNS is configured.

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Create a Cloud Storage bucket, then enable Cloud CDN on the bucket, then create an external HTTP(S) load balancer with the bucket as a backend, then update DNS records.

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Create an external HTTP(S) load balancer, then create a Cloud Storage bucket, then enable Cloud CDN on the bucket, then update DNS records.

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Enable Cloud CDN on a bucket, then create an external HTTP(S) load balancer, then add the bucket as a backend, then update DNS records.

Question 8

Match each Cloud Storage class to its typical use case.

Accepted Answer

Standard: Frequently accessed data. Cloud Storage classes optimize cost based on access frequency. Standard is for hot data, Nearline for monthly, Coldline for quarterly, and Archive for yearly access. Common mistakes involve confusing the frequency thresholds.

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Standard: Data accessed less than once a year

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Nearline: Data accessed less than once a quarter

Question 9

Match each command-line tool to its primary use.

Accepted Answer

gcloud: Primary interface for Google Cloud Platform. The correct matches are: gcloud for general GCP management, gsutil for Cloud Storage, kubectl for Kubernetes, and bq for BigQuery. Common confusions include mixing up gcloud with gsutil, or kubectl with bq.

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gcloud: Manage Cloud Storage buckets and objects

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kubectl: Run queries and manage BigQuery datasets

Question 10

A developer is setting up a Cloud Build configuration file for a Node.js application. They want to ensure that build steps are executed only when changes are pushed to the 'main' branch. What is the correct approach?

Accepted Answer

Use a build trigger with a branch filter. Cloud Build triggers can be configured with a branch filter (e.g., `^main$`) that ensures builds are only initiated when changes are pushed to the specified branch. This is the native, declarative way to control build execution based on Git branch events, without requiring custom scripting or external scheduling.

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Use a script in the build step to check the branch name

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Use Cloud Scheduler to trigger builds based on time intervals

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Use a condition in the build config file

Question 11

A development team uses Cloud Build for CI/CD with a monorepo containing multiple microservices. They want to implement a strategy where only the services affected by a commit are built and deployed. Which approach best achieves this?

Accepted Answer

Use multiple Cloud Build triggers, one per service, each with a path filter for its directory. Cloud Build triggers support path filters that allow you to specify which directories or files should initiate a build. By creating one trigger per microservice directory, only the service whose code has changed will be built and deployed, which is the most efficient and native approach for a monorepo with multiple services.

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Use a single Cloud Build trigger with a condition to check changed files

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Use Cloud Functions to detect changes and trigger builds

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Use a single Cloud Build trigger with a bash script to detect changes

Question 12

A developer is writing a Cloud Function that throws an exception when processing invalid input. They want to ensure the function returns an appropriate HTTP error response. What should they do?

Accepted Answer

Return a response with a status code and error message from the function. In Cloud Functions (and serverless platforms like Google Cloud Functions), the function code itself is responsible for constructing and returning an HTTP response, including setting the appropriate status code and error message. Throwing an exception alone does not automatically map to an HTTP error response; the platform will typically return a generic 500 error, which is not appropriate for invalid input (e.g., 400 Bad Request). By explicitly returning a response object with a status code (e.g., 400) and a descriptive error message, the developer ensures the client receives a meaningful and correct HTTP error response.

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Log the error and return a success response

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Use a global error handler in the function framework

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Throw an exception and let the platform handle it automatically

Question 13

A company uses Cloud Build to build Docker images and push them to Artifact Registry. They want to ensure that only images built from a specific Cloud Build trigger are deployed to production. Which combination of steps should they implement?

Accepted Answer

Configure Binary Authorization with a policy requiring attestation from Cloud Build. Binary Authorization enforces deployment-time policies that require images to have a valid attestation from an approved authority. By configuring Cloud Build to create an attestation for images built from the specific trigger, and setting a Binary Authorization policy that requires that attestation, only those attested images can be deployed to production. This directly ensures that only images from that trigger are used.

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Configure IAM roles to restrict push access to Artifact Registry

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Use Cloud Deploy with a manual approval gate

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Use Cloud Build's built-in approval mechanism

Question 14

A developer is writing unit tests for a Cloud Function that reads from Firestore. They want to avoid real Firestore calls in tests. Which approach is best?

Accepted Answer

Mock the Firestore client library in the test code. Mocking the Firestore client library in unit tests is the best approach because it isolates the function's logic from external dependencies, allowing tests to run quickly and deterministically without any network calls. By replacing the Firestore client with a mock, developers can simulate various responses and error conditions, ensuring the unit test focuses solely on the function's code. This aligns with the principle of unit testing: test a single unit in isolation, not its integrations.

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Use Cloud Functions local emulator with Firestore emulator

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Create a test project with real Firestore and use real calls

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Use Firestore emulator for tests

Question 15

A Cloud Run service experiences high latency under load. The service is a Node.js Express app that processes requests sequentially due to a global mutex. What is the most effective solution?

Accepted Answer

Remove the mutex and ensure request handling is asynchronous. The root cause is that the global mutex forces sequential processing, negating Node.js's asynchronous event loop. Removing the mutex and ensuring asynchronous request handling (e.g., using async/await or Promises) allows the single-threaded event loop to interleave I/O-bound tasks, dramatically reducing latency under concurrent load. This directly addresses the bottleneck without changing the underlying infrastructure.

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Use Cloud Run for Anthos to handle load

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Increase the number of CPUs per container

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Increase the 'max-instances' setting