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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing secure applications 1. Securing data in transit and at rest 2. Implementing authentication and authorization mechanisms 3. Applying security best practices and compliance requirements - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting - Designing high-performing applications and APIs 1. Designing for high availability and fault tolerance 2. Optimizing for latency and throughput 3. Selecting appropriate compute and deployment platforms
Building and testing applications	23%	- Setting up development environments 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations 2. Emulating Google Cloud services locally - Testing applications 1. Automating testing in CI/CD workflows 2. Writing unit, integration, and end-to-end tests 3. Using AI-assisted development tools - Building applications and containers 1. Using Cloud Build and Artifact Registry 2. Implementing containerization best practices 3. Configuring build pipelines and provenance
Configuring cloud-native applications for deployment	24%	- Deploying to App Engine or Cloud Functions 1. Configuring serverless deployments 2. Managing versions and traffic splitting - Deploying applications to Cloud Run 1. Deploying from source code or containers 2. Configuring scaling, networking, and security 3. Integrating with event-driven services - Deploying applications to Google Kubernetes Engine 1. Creating and managing workloads and services 2. Configuring scaling, auto-scaling, and updates 3. Managing configurations and secrets
Integrating applications with Google Cloud services	21%	- Consuming Google Cloud APIs and services 1. Using client libraries, REST, and gRPC 2. Integrating with AI/ML and generative AI services 3. Enabling and managing APIs - Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - Integrating with data and storage services 1. Implementing messaging and event streaming 2. Using object storage and file storage solutions 3. Working with relational, NoSQL, and in-memory databases

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Google Certified Professional - Cloud Developer Sample Questions (Q337-Q342):

NEW QUESTION # 337
Your application is built as a custom machine image. You have multiple unique deployments of the machine image. Each deployment is a separate managed instance group with its own template. Each deployment requires a unique set of configuration values. You want to provide these unique values to each deployment but use the same custom machine image in all deployments. You want to use out-of-the-box features of Compute Engine. What should you do?

A. Place the unique configuration values in the persistent disk.
B. Place the unique configuration values in a Cloud Bigtable table.
C. Place the unique configuration values in the instance template instance metadata.
D. Place the unique configuration values in the instance template startup script.

Answer: A

Explanation:
Reference:
https://cloud.google.com/compute/docs/instance-groups

NEW QUESTION # 338
Your team is developing a new application that is packaged as a container and stored in Artifact Registry. You are responsible for configuring the CI/CD pipelines that use Cloud Build.
Containers may be pushed manually as a local development effort or in an emergency. Every time a new container is pushed to Artifact Registry, you need to trigger another Cloud Build pipeline that executes a vulnerability scan. You want to implement this requirement using the least amount of effort. What should you do?

A. Configure Artifact Registry to publish a message to a Pub/Sub topic when a new image is pushed. Configure the vulnerability scan pipeline to be triggered by the Pub/Sub message.
B. Configure the Cloud Build CI pipeline that publishes the new image to send a message to a Pub/Sub topic that triggers the vulnerability scan pipeline.
C. Use Cloud Scheduler to periodically check for new versions of the container in Artifact Registry and trigger the vulnerability scan pipeline.
D. Configure Artifact Registry to publish a message to a Pub/Sub topic when a new image is pushed. Configure Pub/Sub to invoke a Cloud Run function that triggers the vulnerability scan pipeline.

Answer: A

Explanation:
This approach requires the least amount of effort and utilizes built-in integrations within Google Cloud. By configuring Artifact Registry to publish a message to a Pub/Sub topic each time a new image is pushed, you can directly trigger the vulnerability scan pipeline with this Pub/Sub message. This solution is efficient because it automates the process without requiring additional components like Cloud Functions or Cloud Scheduler, and it only triggers when new images are pushed, meeting your requirement for scanning upon each image push.

NEW QUESTION # 339
Your company stores their source code in a Cloud Source Repositories repository. Your company wants to build and test their code on each source code commit to the repository and requires a solution that is managed and has minimal operations overhead.
Which method should they use?

A. Use Jenkins deployed via the Google Cloud Platform Marketplace, configured to watch for source code commits.
B. Use a source code commit trigger to push a message to a Cloud Pub/Sub topic that triggers an App Engine service to build the source code.
C. Use a Compute Engine virtual machine instance with an open source continuous integration tool, configured to watch for source code commits.
D. Use Cloud Build with a trigger configured for each source code commit.

Answer: D

Explanation:
https://cloud.google.com/build/docs/automating-builds/create-manage-triggers#:~:text=A%20Cloud%20Build%2

NEW QUESTION # 340
You are building a CI/CD pipeline that consists of a version control system, Cloud Build, and Container Registry. Each time a new tag is pushed to the repository, a Cloud Build job is triggered, which runs unit tests on the new code builds a new Docker container image, and pushes it into Container Registry. The last step of your pipeline should deploy the new container to your production Google Kubernetes Engine (GKE) cluster. You need to select a tool and deployment strategy that meets the following requirements:
* Zero downtime is incurred
* Testing is fully automated
* Allows for testing before being rolled out to users
* Can quickly rollback if needed
What should you do?

A. Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a canary test.
B. Trigger a Spinnaker pipeline configured as a canary test of your new code and, if it is successful, deploy the container to production.
C. Trigger another Cloud Build job that uses the Kubernetes CLI tools to deploy your new container to your GKE cluster, where you can perform a shadow test.
D. Trigger a Spinnaker pipeline configured as an A/B test of your new code and, if it is successful, deploy the container to production.

Answer: C

Explanation:
https://cloud.google.com/architecture/implementing-deployment-and-testing-strategies-on-gke#perform_a_shadow_test With a shadow test, you test the new version of your application by mirroring user traffic from the current application version without impacting the user requests.

NEW QUESTION # 341
You maintain a CI/CD pipeline for an application running on GKE. You use Cloud Build to create container images and push the images to Artifact Registry. When you build the image, you use the latest tag in your pipeline.
You recently had to roll back a deployment 24 hours after rollout. The rollback process was difficult because the latest tag had been overwritten. You need to prevent this issue in the future.
You want to use the most efficient approach. What should you do?

A. Rebuild the Docker image for each environment, and tag it with the specific environment name.
B. Implement Helm charts to manage your container deployments.
C. Use a Docker registry to store the container images, and create separate repositories for each version of the application.
D. Build a separate Docker image for each new version of the application, and tag it with the version number.

Answer: D

Explanation:
Tagging images with a unique version number for each build allows you to track specific versions of your application and roll back easily if needed. This approach avoids the issue of the latest tag being overwritten, as each versioned image is uniquely identifiable and immutable once built. By tagging images with version numbers, you can manage deployments and rollbacks more efficiently without ambiguity about which image version is deployed.
Using Helm charts could help with deployment management but does not address the issue of image version tracking. Similarly, separate repositories for each version would add unnecessary complexity.

NEW QUESTION # 342
......

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