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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 for observability and monitoring 1. Designing for debugging and troubleshooting 2. Planning logging, metrics, and tracing strategies - 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 - 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
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. Integrating with event-driven services 2. Configuring scaling, networking, and security 3. Deploying from source code or containers - 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
Integrating applications with Google Cloud services	21%	- 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 - Integrating with data and storage services 1. Using object storage and file storage solutions 2. Working with relational, NoSQL, and in-memory databases 3. Implementing messaging and event streaming - Implementing application performance monitoring 1. Instrumenting code for metrics and traces 2. Using operations suite tools
Building and testing applications	23%	- Testing applications 1. Automating testing in CI/CD workflows 2. Using AI-assisted development tools 3. Writing unit, integration, and end-to-end tests - Building applications and containers 1. Configuring build pipelines and provenance 2. Using Cloud Build and Artifact Registry 3. Implementing containerization best practices - Setting up development environments 1. Emulating Google Cloud services locally 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations

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Google Certified Professional - Cloud Developer Sample Questions (Q244-Q249):

NEW QUESTION # 244
Your company has deployed a new API to a Compute Engine instance. During testing, the API is not behaving as expected. You want to monitor the application over 12 hours to diagnose the problem within the application code without redeploying the application. Which tool should you use?

A. Cloud Monitoring
B. Cloud Trace
C. Cloud Debugger logpoints
D. Cloud Debugger snapshots

Answer: C

Explanation:
Explanation
https://cloud.google.com/debugger/docs/using/logpoints
Logpoints allow you to inject logging into running services without restarting or interfering with the normal function of the service

NEW QUESTION # 245
You are using Cloud Build to build a Docker image. You need to modify the build to execute unit and run integration tests. When there is a failure, you want the build history to clearly display the stage at which the build failed.
What should you do?

A. Create a Cloud Build build config file that will spawn a separate cloud build pipeline for unit and integration tests.
B. Create a Cloud Build build config file with separate cloud builder steps to compile and execute unit and integration tests.
C. Create a Cloud Build build config file with a single build step to compile unit and integration tests.
D. Add RUN commands in the Dockerfile to execute unit and integration tests.

Answer: B

Explanation:
https://cloud.google.com/build/docs/build-config-file-schema
A build step specifies an action that you want Cloud Build to perform. For each build step, Cloud Build executes a docker container as an instance of docker run. Build steps are analogous to commands in a script and provide you with the flexibility of executing arbitrary instructions in your build.

NEW QUESTION # 246
You need to configure a Deployment on Google Kubernetes Engine (GKE). You want to include a check that verifies that the containers can connect to the database. If the Pod is failing to connect, you want a script on the container to run to complete a graceful shutdown. How should you configure the Deployment?

A. Create the Deployment with a PostStart lifecycle handler that checks the service availability. Configure a PreStop lifecycle handler that runs the shutdown script if the container is failing.
B. Create the Deployment with a livenessProbe for the container that will fail if the container can't connect to the database. Configure a Prestop lifecycle handler that runs the shutdown script if the container is failing.
C. Create the Deployment with an initContainer that checks the service availability. Configure a Prestop lifecycle handler that runs the shutdown script if the Pod is failing.
D. Create two jobs: one that checks whether the container can connect to the database, and another that runs the shutdown script if the Pod is failing.

Answer: B

Explanation:
Explanation
https://cloud.google.com/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke#m

NEW QUESTION # 247
You are a developer working on an internal application for payroll processing. You are building a component of the application that allows an employee to submit a timesheet, which then initiates several steps:
- An email is sent to the employee and manager, notifying them that the timesheet was submitted.
- A timesheet is sent to payroll processing for the vendor's API.
- A timesheet is sent to the data warehouse for headcount planning.
These steps are not dependent on each other and can be completed in any order. New steps are being considered and will be implemented by different development teams. Each development team will implement the error handling specific to their step. What should you do?

A. Create a timesheet microservice deployed to Google Kubernetes Engine. The microservice calls each downstream step and waits for a successful response before calling the next step.
B. Deploy a Cloud Function for each step that calls the corresponding downstream system to complete the required action.
C. Create a Pub/Sub topic for timesheet submissions. Create a subscription for each downstream development team to subscribe to the topic.
D. Create a Pub/Sub topic for each step. Create a subscription for each downstream development team to subscribe to their step's topic.

Answer: C

Explanation:
Pub/Sub is a messaging service that allows you to decouple microservices and other applications. It is a good choice for this use case because it is scalable, reliable, and easy to use.

NEW QUESTION # 248
You are supporting a business-critical application in production deployed on Cloud Run. The application is reporting HTTP 500 errors that are affecting the usability of the application. You want to be alerted when the number of errors exceeds 15% of the requests within a specific time window. What should you do?

A. Navigate to the Cloud Run page in the Google Cloud console, and select the service from the services list.
Use the Metrics tab to visualize the number of errors for that revision and refresh the page daily.
B. Create a Cloud Function that consumes the Cloud Monitoring API Use Cloud Scheduler to trigger the Cloud Function daily and alert you if the number of errors is above the defined threshold
C. Create an alerting policy in Cloud Monitoring that alerts you if the number of errors is above the defined threshold.
D. Create a Cloud Function that consumes the Cloud Monitoring API Use Cloud Composer to trigger the Cloud Function daily and alert you if the number of errors is above the defined threshold.

Answer: D

NEW QUESTION # 249
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