You have an application running in a production Google Kubernetes Engine (GKE) cluster. You use Cloud Deploy to automatically deploy your application to your production GKE cluster. As part of your development process: you are planning to make frequent changes to the applications source code and need to select the tools to test the changes before pushing them to your remote source code repository. Your toolset must meet the following requirements:
• Test frequent local changes automatically.
• Local deployment emulates production deployment.
Which tools should you use to test building and running a container on your laptop using minimal resources'?
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.)
You have recently instrumented a new application with OpenTelemetry, and you want to check the latency of your application requests in Trace. You want to ensure that a specific request is always traced. What should you do?
Your company’s corporate policy states that there must be a copyright comment at the very beginning of all source files. You want to write a custom step in Cloud Build that is triggered by each source commit. You need the trigger to validate that the source contains a copyright and add one for subsequent steps if not there. What should you do?