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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Configuring cloud-native applications for deployment	24%	- Deploying applications to Cloud Run 1. Integrating with event-driven services 2. Configuring scaling, networking, and security 3. Deploying from source code or containers - Deploying to App Engine or Cloud Functions 1. Managing versions and traffic splitting 2. Configuring serverless deployments - Deploying applications to Google Kubernetes Engine 1. Managing configurations and secrets 2. Configuring scaling, auto-scaling, and updates 3. Creating and managing workloads and services
Topic 2: Building and testing applications	23%	- Building applications and containers 1. Implementing containerization best practices 2. Using Cloud Build and Artifact Registry 3. Configuring build pipelines and provenance - Setting up development environments 1. Emulating Google Cloud services locally 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations - Testing applications 1. Writing unit, integration, and end-to-end tests 2. Automating testing in CI/CD workflows 3. Using AI-assisted development tools
Topic 3: Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing for observability and monitoring 1. Designing for debugging and troubleshooting 2. Planning logging, metrics, and tracing strategies - 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. Applying security best practices and compliance requirements 2. Securing data in transit and at rest 3. Implementing authentication and authorization mechanisms
Topic 4: Integrating applications with Google Cloud services	21%	- Integrating with data and storage services 1. Implementing messaging and event streaming 2. Working with relational, NoSQL, and in-memory databases 3. Using object storage and file storage solutions - Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - 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

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Google Certified Professional - Cloud Developer Sample Questions (Q238-Q243):

NEW QUESTION # 238
You want to create "fully baked" or "golden" Compute Engine images for your application. You need to bootstrap your application to connect to the appropriate database according to the environment the application is running on (test, staging, production). What should you do?

A. Embed the appropriate database connection string in the image. Create a different image for each environment.
B. When creating the Compute Engine instance, create a metadata item with a key of "DATABASE" and a value for the appropriate database connection string. In your application, query the metadata server for the "DATABASE" value, and use the value to connect to the appropriate database.
C. When creating the Compute Engine instance, add a tag with the name of the database to be connected.
In your application, query the Compute Engine API to pull the tags for the current instance, and use the tag to construct the appropriate database connection string.
D. When creating the Compute Engine instance, create a metadata item with a key of "DATABASE" and a value for the appropriate database connection string. In your application, read the "DATABASE" environment variable, and use the value to connect to the appropriate database.

Answer: D

NEW QUESTION # 239
You have developed a Python application that you want to containerize and deploy to Cloud Run.
You have developed a Cloud Build pipeline with the following steps:

After triggering the pipeline, you notice in the Cloud Build logs that the final step of the pipeline fails and the container is unable to be deployed to Cloud Run. What is the cause of this issue, and how should you resolve it?

A. The Docker container image has not been pushed to Artifact Registry. Add a step to the pipeline to push the application container image to Artifact Registry, and rerun the pipeline.
B. Unit tests in the pipeline are failing. Update the application code so that all unit tests pass, and rerun the pipeline.
C. Cloud Run does not allow unauthenticated invocations. Remove the --allow-unauthenticated parameter to enforce authentication on the application, and rerun the pipeline.
D. The final step uses a Cloud Run instance name that does not match the container name. Update the deployment step so that the Cloud Run instance name matches the container name, and rerun the pipeline.

Answer: A

Explanation:
The pipeline steps in the image show that the Docker image is built, but there is no step to push the built image to Artifact Registry. Cloud Run requires the container image to be in a registry (such as Artifact Registry or Container Registry) before it can be deployed. Adding a step to push the container image to Artifact Registry after it is built will make the image available for deployment, allowing the final step to deploy the application to Cloud Run successfully.

NEW QUESTION # 240
Your application takes an input from a user and publishes it to the user's contacts. This input is stored in a table in Cloud Spanner. Your application is more sensitive to latency and less sensitive to consistency.
How should you perform reads from Cloud Spanner for this application?

A. Perform stale reads using read-write transactions.
B. Perform strong reads using single-read methods.
C. Perform Read-Only transactions.
D. Perform stale reads using single-read methods.

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/best-practices-cloud-spanner-gaming-database

NEW QUESTION # 241
You are load testing your server application. During the first 30 seconds, you observe that a previously inactive Cloud Storage bucket is now servicing 2000 write requests per second and
7500 read requests per second. Your application is now receiving intermittent 5xx and 429 HTTP responses from the Cloud Storage JSON API as the demand escalates. You want to decrease the failed responses from the Cloud Storage API.
What should you do?

A. Distribute the uploads across a large number of individual storage buckets.
B. Pass the HTTP response codes back to clients that are invoking the uploads from your application.
C. Limit the upload rate from your application clients so that the dormant bucket's peak request rate is reached more gradually.
D. Use the XML API instead of the JSON API for interfacing with Cloud Storage.

Answer: C

Explanation:
If you run into any issues such as increased latency or error rates, pause your ramp-up or reduce the request rate temporarily in order to give Cloud Storage more time to scale your bucket. You should use exponential backoff to retry your requests when:
Receiving errors with 5xx and 429 response codes.
Receiving errors with 408 response codes when performing resumable uploads.
https://cloud.google.com/storage/docs/request-rate#ramp-up

NEW QUESTION # 242
Your website is deployed on Compute Engine. Your marketing team wants to test conversion rates between 3 different website designs.
Which approach should you use?

A. Deploy the website on Cloud Functions and use traffic splitting.
B. Deploy the website on Cloud Functions as three separate functions.
C. Deploy the website on App Engine and use traffic splitting.
D. Deploy the website on App Engine as three separate services.

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic

NEW QUESTION # 243
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