QUESTION: 137

You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud
Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale.
What should you do?

Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.
Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.

Answer(s): B

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QUESTION: 138

You are using Cloud Build for your CI/CD pipeline to complete several tasks, including copying certain files to Compute Engine virtual machines. Your pipeline requires a flat file that is generated in one builder in the pipeline to be accessible by subsequent builders in the same pipeline. How should you store the file so that all the builders in the pipeline can access it?

Store and retrieve the file contents using Compute Engine instance metadata.
Output the file contents to a file in /workspace. Read from the same /workspace file in the subsequent build step.
Use gsutil to output the file contents to a Cloud Storage object. Read from the same object in the subsequent build step.
Add a build argument that runs an HTTP POST via curl to a separate web server to persist the value in one builder. Use an HTTP GET via curl from the subsequent build step to read the value.

Answer(s): B

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QUESTION: 139

Your company’s development teams want to use various open source operating systems in their Docker builds.
When images are created in published containers in your company’s environment, you need to scan them for Common Vulnerabilities and Exposures (CVEs). The scanning process must not impact software development agility. You want to use managed services where possible.
What should you do?

Enable the Vulnerability scanning setting in the Container Registry.
Create a Cloud Function that is triggered on a code check-in and scan the code for CVEs.
Disallow the use of non-commercially supported base images in your development environment.
Use Cloud Monitoring to review the output of Cloud Build to determine whether a vulnerable version has been used.

Answer(s): A

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QUESTION: 140

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.)

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

Answer(s): A,C

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Free Professional Cloud Developer Exam Braindumps (page: 35)

QUESTION: 137

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QUESTION: 139

QUESTION: 140