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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. 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
Topic 2: 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. Configuring build pipelines and provenance 2. Using Cloud Build and Artifact Registry 3. Implementing containerization best practices
Topic 3: 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. Managing versions and traffic splitting 2. Configuring serverless deployments - 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
Topic 4: 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. Selecting appropriate compute and deployment platforms 2. Designing for high availability and fault tolerance 3. Optimizing for latency and throughput - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting

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Google Certified Professional - Cloud Developer Sample Questions (Q141-Q146):

NEW QUESTION # 141
You are building a new API. You want to minimize the cost of storing and reduce the latency of serving images.
Which architecture should you use?

A. Cloud Content Delivery Network (CDN) backed by Cloud Storage
B. Transfer Appliance backed by Cloud Filestore
C. App Engine backed by Cloud Storage
D. Compute Engine backed by Persistent Disk

Answer: D

NEW QUESTION # 142
You are configuring a continuous integration pipeline using Cloud Build to automate the deployment of new container images to Google Kubernetes Engine (GKE). The pipeline builds the application from its source code, runs unit and integration tests in separate steps, and pushes the container to Container Registry. The application runs on a Python web server.
The Dockerfile is as follows:
FROM python:3.7-alpine
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD [ "gunicorn", "-w 4", "main:app" ]
You notice that Cloud Build runs are taking longer than expected to complete. You want to decrease the build time. What should you do? (Choose two.)

A. Select a virtual machine (VM) size with higher CPU for Cloud Build runs.
B. Change the base image in the Dockerfile to ubuntu:latest, and install Python 3.7 using a package manager utility.
C. Deploy a Container Registry on a Compute Engine VM in a VPC, and use it to store the final images.
D. Cache the Docker image for subsequent builds using the -- cache-from argument in your build config file.
E. Store application source code on Cloud Storage, and configure the pipeline to use gsutil to download the source code.

Answer: A,D

Explanation:
https://cloud.google.com/build/docs/optimize-builds/increase-vcpu-for-builds By default, Cloud Build runs your builds on a standard virtual machine (VM). In addition to the standard VM, Cloud Build provides several high-CPU VM types to run builds. To increase the speed of your build, select a machine with a higher vCPU to run builds. Keep in mind that although selecting a high vCPU machine increases your build speed, it may also increase the startup time of your build as Cloud Build only starts non-standard machines on demand.
https://cloud.google.com/build/docs/optimize-builds/speeding-up-
builds#using_a_cached_docker_image
The easiest way to increase the speed of your Docker image build is by specifying a cached image that can be used for subsequent builds. You can specify the cached image by adding the -- cache-from argument in your build config file, which will instruct Docker to build using that image as a cache source.

NEW QUESTION # 143
You work for an ecommerce company, and you are responsible for deploying and managing multiple APIs. The operations team wants to review the traffic patterns in the orders-prod and users-prod environments. These are the only environments in the store-prod environment group.
You want to follow Google-recommended practices. What should you do?

A. Assign the Apigee API Reader IAM role to each user of the operations team for both environments. Use Cloud Monitoring to review traffic patterns.
B. Assign the Apigee API Reader IAM role to each user of the operations team for both environments. Use Apigee API Analytics to review traffic patterns.
C. Assign the Apigee Analytics Viewer IAM role to the operations team for both environments. Use Cloud Monitoring to review traffic patterns.
D. Assign the Apigee Analytics Viewer IAM role to the operations team for both environments. Use Apigee API Analytics to review traffic patterns.

Answer: D

Explanation:
https://cloud.google.com/blog/products/api-management/api-monitoring-and-analytics-for-2021

NEW QUESTION # 144
You have an application deployed in production. When a new version is deployed, some issues don't arise until the application receives traffic from users in production. You want to reduce both the impact and the number of users affected.
Which deployment strategy should you use?

A. Blue/green deployment
B. Recreate deployment
C. Rolling deployment
D. Canary deployment

Answer: A

NEW QUESTION # 145
You are developing a new web application using Cloud Run and committing code to Cloud Source Repositories. You want to deploy new code in the most efficient way possible. You have already created a Cloud Build YAML file that builds a container and runs the following command: gcloud run deploy. What should you do next?

A. Create a Cron job that runs the following command every 24 hours: gcloud builds submit.
B. Create a webhook build trigger that runs the build file in response to HTTP POST calls to the webhook URL.
C. Create a Pub/Sub topic to be notified when code is pushed to the repository. Create a Pub/Sub trigger that runs the build file when an event is published to the topic.
D. Create a build trigger that runs the build file in response to a repository code being pushed to the development branch.

Answer: D

Explanation:
https://cloud.google.com/build/docs/triggers
Cloud Build uses build triggers to enable CI/CD automation. You can configure triggers to listen for incoming events, such as when a new commit is pushed to a repository or when a pull request is initiated, and then automatically execute a build when new events come in. You can also configure triggers to build code on any changes to your source repository or only on changes that match certain criteria.

NEW QUESTION # 146
......

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