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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. Using object storage and file storage solutions 3. Working with relational, NoSQL, and in-memory databases - Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - Consuming Google Cloud APIs and services 1. Integrating with AI/ML and generative AI services 2. Using client libraries, REST, and gRPC 3. Enabling and managing APIs
Topic 2: Configuring cloud-native applications for deployment	24%	- 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 - Deploying to App Engine or Cloud Functions 1. Managing versions and traffic splitting 2. Configuring serverless deployments - Deploying applications to Cloud Run 1. Configuring scaling, networking, and security 2. Integrating with event-driven services 3. Deploying from source code or containers
Topic 3: Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing secure applications 1. Implementing authentication and authorization mechanisms 2. Applying security best practices and compliance requirements 3. Securing data in transit and at rest - Designing high-performing applications and APIs 1. Optimizing for latency and throughput 2. Selecting appropriate compute and deployment platforms 3. Designing for high availability and fault tolerance - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting
Topic 4: Building and testing applications	23%	- Testing applications 1. Using AI-assisted development tools 2. Automating testing in CI/CD workflows 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 - Building applications and containers 1. Implementing containerization best practices 2. Configuring build pipelines and provenance 3. Using Cloud Build and Artifact Registry

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Google Certified Professional - Cloud Developer Sample Questions (Q30-Q35):

NEW QUESTION # 30
You have containerized a legacy application that stores its configuration on an NFS share. You need to deploy this application to Google Kubernetes Engine (GKE) and do not want the application serving traffic until after the configuration has been retrieved. What should you do?

A. Use the gsutil utility to copy files from within the Docker container at startup, and start the service using an ENTRYPOINT script.
B. Add a startup script to the GKE instance group to mount the NFS share at node startup. Copy the configuration files into the container, and start the service using an ENTRYPOINT script.
C. Create a PersistentVolumeClaim on the GKE cluster. Access the configuration files from the volume, and start the service using an ENTRYPOINT script.
D. Use the COPY statement in the Dockerfile to load the configuration into the container image. Verify that the configuration is available, and start the service using an ENTRYPOINT script.

Answer: B

NEW QUESTION # 31
You want to upload files from an on-premises virtual machine to Google Cloud Storage as part of a data migration. These files will be consumed by Cloud DataProc Hadoop cluster in a GCP environment.
Which command should you use?

A. hadoop fs cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
B. gcloud cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
C. gcloud dataproc cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
D. gsutil cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/

Answer: D

Explanation:
Explanation
The gsutil cp command allows you to copy data between your local file. storage. boto files generated by running "gsutil config"

NEW QUESTION # 32
For this question, refer to the HipLocal case study.
Which Google Cloud product addresses HipLocal's business requirements for service level indicators and objectives?

A. Cloud Logging
B. Cloud Monitoring
C. Cloud Profiler
D. Cloud Trace

Answer: B

Explanation:
Explanation
https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring#defn-sli

NEW QUESTION # 33
Your team manages a Google Kubernetes Engine (GKE) cluster where an application is running. A different team is planning to integrate with this application. Before they start the integration, you need to ensure that the other team cannot make changes to your application, but they can deploy the integration on GKE. What should you do?

A. Using Identity and Access Management (IAM), grant the Viewer IAM role on the cluster project to the other team.
B. Create a new GKE cluster. Using Identity and Access Management (IAM), grant the Editor role on the cluster project to the other team.
C. Create a new namespace in the existing cluster. Using Kubernetes role-based access control (RBAC), grant the Admin role on the new namespace to the other team.
D. Create a new namespace in the existing cluster. Using Identity and Access Management (IAM), grant the Editor role on the cluster project to the other team.

Answer: C

NEW QUESTION # 34
Your teammate has asked you to review the code below. Its purpose is to query account entities in Cloud Datastore for those with a balance greater than 10000 and an age less than 4. Which improvement should you suggest your teammate make?
Captionless Image

A. Change the argument to OrderBy.desc to be "balance" instead of "age."
B. The query needs to have an ancestor query.
C. Send two queries-one for balances over 10000, and another for ages less than 4-and compute the intersection.
D. The query should be performed in a transaction.

Answer: C

Explanation:
A is not correct because the stated goal doesn't require an ancestor query.
B is not correct because no data is being written, so a transaction isn't necessary.
C is not correct because it wouldn't affect the results.
D is correct because two inequality comparisons aren't permitted in a Datastore query and it requires two queries to be merged.

NEW QUESTION # 35
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