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Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Developer Sample Questions (Q253-Q258):

NEW QUESTION # 253
You are developing a new application that has the following design requirements:
Creation and changes to the application infrastructure are versioned and auditable.
The application and deployment infrastructure uses Google-managed services as much as possible.
The application runs on a serverless compute platform.
How should you design the application's architecture?

A. 1. Deploy Jenkins from the Google Cloud Marketplace, and define a continuous integration pipeline in Jenkins.
2. Configure a pipeline step to pull the application source code from a Git repository.
3. Deploy the application source code to App Engine as a pipeline step.
B. 1. Create a continuous integration pipeline on Cloud Build, and configure the pipeline to deploy the application infrastructure using Deployment Manager templates.
2. Configure a pipeline step to create a container with the latest application source code.
3. Deploy the container to a Compute Engine instance as a pipeline step.
C. 1. Store the application and infrastructure source code in a Git repository.
2. Use Cloud Build to deploy the application infrastructure with Terraform.
3. Deploy the application to a Cloud Function as a pipeline step.
D. 1. Deploy the application infrastructure using gcloud commands.
2. Use Cloud Build to define a continuous integration pipeline for changes to the application source code.
3. Configure a pipeline step to pull the application source code from a Git repository, and create a containerized application.

Answer: D

Explanation:
4. Deploy the new container on Cloud Run as a pipeline step.

NEW QUESTION # 254
In order for HipLocal to store application state and meet their stated business requirements, which database service should they migrate to?

A. Cloud Datastore
B. Separate Cloud SQL clusters for each region
C. Cloud Spanner
D. Cloud Memorystore as a cache

Answer: B

Explanation:
Topic 1, HipLocal
Executive statement
We are the number one local community app; it's time to take our local community services global. Our venture capital investors want to see rapid growth and the same great experience for new local and virtual communities that come online, whether their members are 10 or 10,000 miles away from each other.
Solution concept
HipLocal wants to expand their existing service, with updated functionality, in new regions to better serve their global customers. They want to hire and train a new team to support these regions in their time zones. They will need to ensure that the application scales smoothly and provides clear uptime data.
Existing technical environment
HipLocal's environment is a mix of on-premises hardware and infrastructure running in Google Cloud Platform. The HipLocal team understands their application well but has limited experience in global scale applications. Their existing technical environment is as follows:
Existing APIs run on Compute Engine virtual machine instances hosted in GCP State is stored in a single instance MySQL database in GCP Data is exported to an on-premises Teradata/Vertica data warehouse Data analytics is performed in an on-premises Hadoop environment The application has no logging There are basic indicators of uptime; alerts are frequently fired when the APIs are unresponsive Business Requirements HipLocal's investors want to expand their footprint and support the increase in demand they are seeing. Their requirements are:
Expand availability of the application to new regions
Increase the number of concurrent users that can be supported
Ensure a consistent experience for users when they travel to different regions Obtain user activity metrics to better understand how to monetize their product Ensure compliance with regulations in the new regions (for example, GDPR) Reduce infrastructure management time and cost Adopt the Google-recommended practices for cloud computing Technical Requirements The application and backend must provide usage metrics and monitoring APIs require strong authentication and authorization Logging must be increased, and data should be stored in a cloud analytics platform Move to serverless architecture to facilitate elastic scaling Provide authorized access to internal apps in a secure manner

NEW QUESTION # 255
You are the lead developer for a company that provides a financial risk calculation API. The API is built on Cloud Run and has a gRPC interface. You frequently develop optimizations to the risk calculators. You want to enable these optimizations for select customers who registered to try out the optimizations prior to rolling out the optimization to all customers. Your CI/CD pipeline has built a new image and stored it in the Artifact Registry. Which rollout strategy should you use?

A. Migrate the traffic to the new service by using a blue/green deployment approach.
B. Migrate the traffic to the new service by using a feature flag for registered customers.
C. Migrate the traffic to the new service and enable session affinity for Cloud Run.
D. Migrate the traffic to the new service by setting Cloud Run's traffic split based on the percentage of registered customers.

Answer: B

Explanation:
https://cloud.google.com/blog/products/serverless/cloud-run-now-supports-gradual-rollouts-and- rollbacks

NEW QUESTION # 256
You are developing a web application that will be deployed to production on Cloud Run. The application consists of multiple microservices, some of which will be publicly accessible and others that will only be accessible after authentication by Google identities. You need to ensure that only authenticated users can access the restricted services, while allowing unrestricted access to the public services of the application. You want to use the most secure approach while minimizing management overhead and complexity. How should you configure access?

A. Configure separate Cloud Run services for the public and restricted microservices. Enable Identity-Aware Proxy (IAP) only for the restricted services, and configure the Cloud Run ingress settings to 'Internal and Cloud Load Balancing'.
B. Use Cloud Endpoints with Firebase Authentication for all microservices. Configure Firebase rules to manage access control lists (ACLs) for each service, allowing access to the public services.
C. Enable Identity-Aware Proxy (IAP) for all microservices. Manage access control lists (ACLs) for the restricted services, and configure allAuthenticatedUsers access to the public services.
D. Enable Identity-Aware Proxy (IAP) for all microservices. Develop a new microservice that checks the authentication requirements for each application and controls access to the respective services.

Answer: A

Explanation:
https://cloud.google.com/run/docs/authenticating/service-to-service

NEW QUESTION # 257
Your service adds text to images that it reads from Cloud Storage. During busy times of the year, requests to Cloud Storage fail with an HTTP 429 "Too Many Requests" status code.
How should you handle this error?

A. Change the storage class of the Cloud Storage bucket to Multi-regional.
B. Add a cache-control header to the objects.
C. Retry the request with a truncated exponential backoff strategy.
D. Request a quota increase from the GCP Console.

Answer: C

Explanation:
A Cloud Storage JSON API usage limit was exceeded. If your application tries to use more than its limit, additional requests will fail. Throttle your client's requests, and/or use truncated exponential backoff.
https://cloud.google.com/storage/docs/json_api/v1/status-codes

NEW QUESTION # 258
......

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