Google Certified Professional - Cloud Developer : Professional-Cloud-Developer

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

You need to load-test a set of REST API endpoints that are deployed to Cloud Run. The API responds to HTTP POST requests. Your load tests must meet the following requirements:
- Load is initiated from multiple parallel threads.
- User traffic to the API originates from multiple source IP addresses.
- Load can be scaled up using additional test instances.
You want to follow Google-recommended best practices. How should you configure the load testing?

• A. Create an image that has cURL installed, and configure cURL to run a test plan. Deploy the image in an unmanaged instance group, and run one instance of the image for each VM.
• B. Deploy a distributed load testing framework on a private Google Kubernetes Engine cluster.
  Deploy additional Pods as needed to initiate more traffic and support the number of concurrent users.
• C. Download the container image of a distributed load testing framework on Cloud Shell.Sequentially start several instances of the container on Cloud Shell to increase the load on the API.
• D. Create an image that has cURL installed, and configure cURL to run a test plan. Deploy the image in a managed instance group, and run one instance of the image for each VM.

You are developing a container build pipeline for an application hosted on GKE. You have the following requirements:
- Only images that are created using your build pipeline should be
deployed on your GKE cluster.
- All code and build artifacts should remain within your environment
and protected from data exfiltration.
How should you build the pipeline?

• A. 1. Create a build pipeline by using Cloud Build with the default worker pool.
  2. Deploy container images to a private container registry in your VPC.
  3. Create a VPC firewall policy in your project that denies all egress and ingress traffic to public networks.
• B. 1. Create a build pipeline by using Cloud Build with the default worker pool.
  2. Configure the CI/CD pipeline to build container images and store them in Artifact Registry.
  3. Configure your GKE cluster to only allow container images signed by Binary Authorization.
• C. 1. Create a build pipeline by using Cloud Build with a private worker pool.
  2. Configure the CI/CD pipeline to build container images and store them in Artifact Registry.
  3. Configure Artifact Registry to encrypt container images by using customer-managed encryption keys (CMEK).
• D. 1. Create a build pipeline by using Cloud Build with a private worker pool.
  2. Use VPC Service Controls to place all components and services in your CI/CD pipeline inside a security perimeter.
  3. Configure your GKE cluster to only allow container images signed by Binary Authorization.

There are three teams developing an ecommerce application in the same Google Cloud project.
Team A will build a set of RESTful APIs that exposes some core functionalities for the application.
Team B and Team C will make requests to those APIs in their downstream processes running on Cloud Run services. You need to propose a solution for exposing the APIs in a way that maximizes security and minimizes management overhead for the three teams. How should you design this solution?

• A. 1. Team A uses Apigee hybrid to create an API key and shares that key with Team B and Team
  2. Team B and Team C store the API key in a secret in Secret Manager, and use this API key to access the API endpoint.
• B. 1. Team A uses service accounts to authorize Cloud API Gateway. Team B and Team C each create a service account that has access to the APIs.
  2. Team B and Team C access the APIs in the Cloud Run service running their processes by using the service accounts attached to their service.
• C. 1. Team A uses service accounts to authorize Cloud API Gateway. Team B and Team C each create a service account, export their service account's key, and store each key in a separate secret in Secret Manager.
  2. Team B and Team C use this service account key to create an OAuth token to access the APIs.
• D. 1. Team A uses an API key to authorize Cloud API Gateway and shares the key with Team B and Team C.
  2. Team B and Team C store the API key in a secret in Secret Manager, and use this API key to access the API endpoint.

Your application in production has recently been experiencing reliability issues, and you are unsure how the application will behave in the event of an unexpected failure. You want to assess the application's resilience. What should you do?

• A. Write end-to-end tests to determine how different microservices interact. Validate that all tests pass.
• B. Perform chaos engineering by intentionally introducing failures into the system. Observe how the application behaves, and ensure that it is able to recover from a failure.
• C. Test individual units of code for a critical portion of the application's code. Ensure that unit tests are part of the Cloud Build pipeline.
• D. Perform load testing of the application, and use JMeter for the critical endpoints of the application.Ensure that the application performs as expected under a heavy load.