2.3 Deploying and Operationalizing the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   Your organization wants to automate the deployment of a Cloud Composer environment and its associated DAGs. You need to manage the environment configuration (e.g., machine types, Airflow configuration overrides) as code, and you want to deploy DAGs to the environment as part of your CI/CD pipeline. Which approach should you use?

   • AUse Cloud Composer's REST API to create the environment and use gcloud commands to upload DAGs manually
   • BUse Cloud Build to create the environment and use Cloud Source Repositories to store DAGs
   • CUse Cloud Scheduler to create the environment and use Cloud Functions to upload DAGs
   • DUse Cloud Deployment Manager to define the Composer environment and use Cloud Build to sync DAGs to the environment's bucket
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2. 17foundation · easy

   Which Google Cloud service is used to store and manage container images and Python packages for use in CI/CD pipelines?

   • ACloud Storage
   • BCloud Source Repositories
   • CCloud Build
   • DArtifact Registry
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4. 18foundation · easy

   What is a primary advantage of using Cloud Composer over Workflows for orchestrating data pipelines?

   • ACloud Composer is serverless and requires no infrastructure management
   • BCloud Composer is better for orchestrating HTTP-based APIs
   • CCloud Composer provides a rich set of operators and integrations for data processing services
   • DCloud Composer is simpler to set up and has a lower learning curve
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5. 19application · medium

   Your CI/CD pipeline for a data pipeline project must include tests that validate the schema of data files before they are processed. You want to run these tests automatically on every pull request. Which approach should you use?

   • AUse Cloud Scheduler to run schema validation nightly
   • BRun schema validation manually before each merge
   • CAdd a Cloud Build step that runs a schema validation script using a test data file
   • DUse Cloud Functions to validate schema when files are uploaded to GCS
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6. 20foundation · easy

   Why is schema validation important in a CI/CD pipeline for data processing?

   • ATo monitor pipeline logs
   • BTo deploy pipeline code to production
   • CTo ensure that data conforms to expected formats and structures before processing
   • DTo test the performance of the pipeline under load
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