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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Building and testing applications	23%	- Setting up development environments 1. Emulating Google Cloud services locally 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations - Testing applications 1. Using AI-assisted development tools 2. Automating testing in CI/CD workflows 3. Writing unit, integration, and end-to-end tests - Building applications and containers 1. Implementing containerization best practices 2. Configuring build pipelines and provenance 3. Using Cloud Build and Artifact Registry
Integrating applications with Google Cloud services	21%	- 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 - Consuming Google Cloud APIs and services 1. Integrating with AI/ML and generative AI services 2. Enabling and managing APIs 3. Using client libraries, REST, and gRPC
Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing high-performing applications and APIs 1. Selecting appropriate compute and deployment platforms 2. Designing for high availability and fault tolerance 3. Optimizing for latency and throughput - 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
Configuring cloud-native applications for deployment	24%	- Deploying applications to Google Kubernetes Engine 1. Configuring scaling, auto-scaling, and updates 2. Creating and managing workloads and services 3. Managing configurations and secrets - Deploying applications to Cloud Run 1. Configuring scaling, networking, and security 2. Integrating with event-driven services 3. Deploying from source code or containers - Deploying to App Engine or Cloud Functions 1. Configuring serverless deployments 2. Managing versions and traffic splitting

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Google Certified Professional - Cloud Developer Sample Questions (Q243-Q248):

NEW QUESTION # 243
Which database should HipLocal use for storing user activity?

A. Cloud Datastore
B. BigQuery
C. Cloud SQL
D. Cloud Spanner

Answer: B

NEW QUESTION # 244
You are tasked with using C++ to build and deploy a microservice for an application hosted on Google Cloud.
The code needs to be containerized and use several custom software libraries that your team has built. You do not want to maintain the underlying infrastructure of the application How should you deploy the microservice?

A. Use Cloud Build to create the container, and deploy it on Cloud Run.
B. Use Cloud Shell to containerize your microservice. and deploy it on a Container-Optimized OS Compute Engine instance.
C. Use Cloud Functions to deploy the microservice.
D. Use Cloud Shell to containerize your microservice. and deploy it on GKE Standard.

Answer: A

NEW QUESTION # 245
You are a developer working with the CI/CD team to troubleshoot a new feature that your team introduced. The CI/CD team used HashiCorp Packer to create a new Compute Engine image from your development branch. The image was successfully built, but is not booting up. You need to investigate the issue with the CI/CD team. What should you do?

A. Shut down the deployed virtual machine, export the disk, and then mount the disk locally to access the boot logs.
B. Create a new feature branch, and ask the build team to rebuild the image.
C. Install Packer locally, build the Compute Engine image locally, and then run it in your personal Google Cloud project.
D. Check Compute Engine OS logs using the serial port, and check the Cloud Logging logs to confirm access to the serial port.

Answer: C

Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-using-serial-console

NEW QUESTION # 246
You need to copy directory local-scripts and all of its contents from your local workstation to a Compute Engine virtual machine instance.
Which command should you use?
gsutil cp --project "my-gcp-project" -r ~/local-scripts/ gcp-instance-name:~/

A. instance-name:~/server-scripts/ --zone "us-east1-b"
gcloud compute mv --project "my-gcp-project" --recurse ~/local-scripts/ gcp-
B. server-scripts/ --zone "us-east1-b"
gcloud compute scp --project "my-gcp-project" --recurse ~/local-scripts/ gcp-
C. server-scripts/ --zone "us-east1-b"
gsutil cp --project "my-gcp-project" -R ~/local-scripts/ gcp-instance-name:~/
D. instance-name:~/server-scripts/ --zone "us-east1-b"

Answer: A

Explanation:
Explanation
Explanation/Reference: https://cloud.google.com/sdk/gcloud/reference/compute/copy-files

NEW QUESTION # 247
You are deploying a Python application to Cloud Run using Cloud Build. The Cloud Build pipeline is shown below:

You want to optimize deployment times and avoid unnecessary steps What should you do?

A. Deploy a new Docker registry in a VPC and use Cloud Build worker pools inside the VPC to run the build pipeline.
B. Add the -cache-from argument to the Docker build step in your build config file.
C. Remove the step that pushes the container to Artifact Registry.
D. Store image artifacts in a Cloud Storage bucket in the same region as the Cloud Run instance.

Answer: B

Explanation:
https://cloud.google.com/build/docs/optimize-builds/speeding-up-builds#using_a_cached_docker_image

NEW QUESTION # 248
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