Google Cloud Professional Data Engineer — Question 231

You are creating the CI/CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

Answer options

• A. 1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.
• B. 1. Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.
• C. 1. Use Cloud Build to build a container and the KubernetesPodOperator to deploy the code of the DAG to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.
• D. 1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

Correct answer: A

Explanation

The correct answer is A because it outlines a straightforward approach of transferring code to the development instance for testing and, upon successful tests, moving it to production. Options B and C involve unnecessary complexity by introducing containerization and GKE deployment, which is not required for the specified task. Option D mixes the correct testing process with an inappropriate production deployment method.