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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Integrating applications with Google Cloud services	21%	- 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 - Integrating with data and storage services 1. Using object storage and file storage solutions 2. Implementing messaging and event streaming 3. Working with relational, NoSQL, and in-memory databases
Topic 2: 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 - Building applications and containers 1. Configuring build pipelines and provenance 2. Using Cloud Build and Artifact Registry 3. Implementing containerization best practices - 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 secure applications 1. Applying security best practices and compliance requirements 2. Securing data in transit and at rest 3. Implementing authentication and authorization mechanisms - 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 for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting
Topic 4: Configuring cloud-native applications for deployment	24%	- Deploying applications to Cloud Run 1. Deploying from source code or containers 2. Integrating with event-driven services 3. Configuring scaling, networking, and security - 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. Configuring serverless deployments 2. Managing versions and traffic splitting

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최신 Cloud Developer Professional-Cloud-Developer 무료샘플문제 (Q103-Q108):

질문 # 103
You are creating and running containers across different projects in Google Cloud. The application you are developing needs to access Google Cloud services from within Google Kubernetes Engine (GKE).
What should you do?

A. Assign a Google service account to the GKE nodes.
B. Use a Google service account with GKE role-based access control (RBAC).
C. Store the Google service account credentials as a Kubernetes Secret.
D. Use a Google service account to run the Pod with Workload Identity.

정답：D

설명：
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity

질문 # 104
You are creating a Google Kubernetes Engine (GKE) cluster and run this command:

The command fails with the error:

You want to resolve the issue. What should you do?

A. Request additional Compute Engine quota in the GCP Console.
B. Open a support case to request additional GKE quotA.
C. Request additional GKE quota is the GCP Console.
D. Decouple services in the cluster, and rewrite new clusters to function with fewer cores.

정답：C

질문 # 105
You are developing a new image processing application that needs to handle various tasks, such as resizing, cropping, and watermarking images. You also need to monitor the workflow and ensure that it scales efficiently when there are large volumes of images. You want to automate the image processing tasks and workflow monitoring with the least effort. What should you do?

A. Employ Cloud Composer to manage the image processing workflows. Use Dataproc for workflow monitoring and analytics.
B. Implement Workflows to orchestrate the image processing tasks. Use Cloud Logging for workflow monitoring.
C. Use Cloud Build to trigger Cloud Run functions for the image processing tasks. Use Cloud Monitoring for workflow monitoring.
D. Use Cloud Run to deploy the image processing functions. Use Apigee to expose the API. Use Cloud Logging for workflow monitoring.

정답：B

설명：
https://cloudinary.com/blog/mediaflows-automate-image-processing

질문 # 106
You have an on-premises application that authenticates to the Cloud Storage API using a user-managed service account with a user-managed key. The application connects to Cloud Storage using Private Google Access over a Dedicated Interconnect link. You discover that requests from the application to access objects in the Cloud Storage bucket are failing with a 403 Permission Denied error code. What is the likely cause of this issue?

A. The service account key has been rotated but not updated on the application server.
B. The Interconnect link from the on-premises data center to Google Cloud is experiencing a temporary outage.
C. The folder structure inside the bucket and object paths have changed.
D. The permissions of the service account's predefined role have changed.

정답：A

질문 # 107
You are using Cloud Build to create a new Docker image on each source code commit to a Cloud Source Repositories repository. Your application is built on every commit to the master branch. You want to release specific commits made to the master branch in an automated method.
What should you do?

A. Manually trigger the build for new releases.
B. Create a build trigger on a Git branch name pattern. Use a Git branch naming convention for new releases.
C. Create a build trigger on a Git tag pattern. Use a Git tag convention for new releases.
D. Commit your source code to a second Cloud Source Repositories repository with a second Cloud Build trigger. Use this repository for new releases only.

정답：B

설명：
Explanation/Reference: https://docs.docker.com/docker-hub/builds/

질문 # 108
......

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