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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - Consuming Google Cloud APIs and services 1. Using client libraries, REST, and gRPC 2. Enabling and managing APIs 3. Integrating with AI/ML and generative AI services - Implementing application performance monitoring 1. Instrumenting code for metrics and traces 2. Using operations suite tools
Topic 2: Configuring cloud-native applications for deployment	24%	- 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 to App Engine or Cloud Functions 1. Configuring serverless deployments 2. Managing versions and traffic splitting - 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
Topic 3: Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing high-performing applications and APIs 1. Optimizing for latency and throughput 2. Designing for high availability and fault tolerance 3. Selecting appropriate compute and deployment platforms - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting - 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
Topic 4: 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. Configuring build pipelines and provenance 2. Implementing containerization best practices 3. Using Cloud Build and Artifact Registry

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Google Certified Professional - Cloud Developer Sample Questions (Q354-Q359):

NEW QUESTION # 354
You are designing a resource-sharing policy for applications used by different teams in a Google Kubernetes Engine cluster. You need to ensure that all applications can access the resources needed to run. What should you do? (Choose two.)

A. Create a Kubernetes service account (KSA) for each application, and assign each KSA to the namespace.
B. Specify the resource limits and requests in the object specifications.
C. Create a namespace for each team, and attach resource quotas to each namespace.
D. Create a LimitRange to specify the default compute resource requirements for each namespace.
E. Use the Anthos Policy Controller to enforce label annotations on all namespaces. Use taints and tolerations to allow resource sharing for namespaces.

Answer: C,D

Explanation:
https://kubernetes.io/docs/concepts/policy/resource-quotas/
https://kubernetes.io/docs/concepts/policy/limit-range/
https://cloud.google.com/blog/products/containers-kubernetes/kubernetes-best-practices-resource-requests-and-li

NEW QUESTION # 355
Your development team has built several Cloud Functions using Java along with corresponding integration and service tests. You are building and deploying the functions and launching the tests using Cloud Build.
Your Cloud Build job is reporting deployment failures immediately after successfully validating the code.
What should you do?

A. Verify that the Cloud Build service account is assigned the Cloud Functions Developer role.
B. Check the maximum number of Cloud Function instances.
C. Retry the tests using the truncated exponential backoff polling strategy.
D. Verify that your Cloud Build trigger has the correct build parameters.

Answer: A

Explanation:
https://cloud.google.com/build/docs/securing-builds/configure-access-for-cloud-build-service-account

NEW QUESTION # 356
Your data is stored in Cloud Storage buckets. Fellow developers have reported that data downloaded from Cloud Storage is resulting in slow API performance. You want to research the issue to provide details to the GCP support team.
Which command should you run?

A. gcloud services test -o output.json gs://my-bucket
B. gsutil test -o output.json gs://my-bucket
C. gsutil perfdiag -o output.json gs:/
D. gcloud compute scp example-instance:~/test-data -o output.json gs://my-bucket

Answer: C

Explanation:
Explanation/Reference: https://groups.google.com/forum/#!topic/gce-discussion/xBl9Jq5HDsY

NEW QUESTION # 357
You have deployed an HTTP(s) Load Balancer with the gcloud commands shown below.

Health checks to port 80 on the Compute Engine virtual machine instance are failing and no traffic is sent to your instances. You want to resolve the problem.
Which commands should you run?

A. gcloud compute instances add-access-config ${NAME}-backend-instance-1
B. gcloud compute firewall-rules create allow-lb --network load-balancer --allow tcp --destination-ranges 130.211.0.0/22,35.191.0.0/16 --direction EGRESS
C. gcloud compute firewall-rules create allow-lb --network load-balancer --allow tcp --source-ranges 130.211.0.0/22,35.191.0.0/16 --direction INGRESS
D. gcloud compute instances add-tags ${NAME}-backend-instance-1 --tags http-server

Answer: C

Explanation:
Reference:
https://cloud.google.com/vpc/docs/special-configurations

NEW QUESTION # 358
You are developing a new API that creates requests on an asynchronous message service.
Requests will be consumed by different services. You need to expose the API by using a gRPC interface while minimizing infrastructure management overhead. How should you deploy the API?

A. Deploy your API to a GKE cluster. Create a Kafka cluster, and configure your API to write messages to the cluster.
B. Deploy your API on a Compute Engine instance. Create a Kafka cluster, and configure your API to write messages to the cluster.
C. Deploy your API to App Engine. Create a Pub/Sub topic, and configure your API to push messages to the topic.
D. Deploy your API as a Cloud Run service. Create a Pub/Sub topic, and configure your API to push messages to the topic.

Answer: D

Explanation:
https://techcommunity.microsoft.com/t5/azure-integration-services-blog/introducing-grpc-api- support-in-azure-api-management-self-hosted/ba-p/3975087

NEW QUESTION # 359
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