What is the main goal of continuous integration (CI) in the context of data pipeline development?

To frequently merge code changes into a shared repository and run automated tests

To manually review and approve all code changes before merging

To automatically deploy pipeline code to production after every commit

To monitor pipeline performance in production

Your data engineering team wants to implement CI/CD for their data pipelines. They currently have a manual process for deploying DAGs to Cloud Composer. They want to automate the deployment and also ensure that the DAGs are syntactically correct before deployment. Which CI/CD practice should they implement?

Use Cloud Source Repositories to run a Python syntax check and then deploy the DAGs

Use Cloud Build to run a Python syntax check on the DAG files and then deploy them to the Composer environment's GCS bucket

Use Cloud Functions to run a Python syntax check and then deploy the DAGs

Use Cloud Scheduler to run a Python syntax check and then deploy the DAGs

What is the primary role of Cloud Composer in a data pipeline architecture?

To provide real-time stream processing for event data

To store and manage large datasets in a distributed file system

To provide a serverless compute platform for running data transformations

To orchestrate and schedule complex workflows as Directed Acyclic Graphs (DAGs)

A Cloud Composer environment is experiencing performance issues. DAG runs are taking longer than expected, and some tasks are being queued for a long time. You suspect that the environment is under-provisioned. You need to improve performance without over-provisioning. Which action should you take first?

Check the Airflow metrics in Cloud Monitoring to identify the bottleneck

Increase the scheduler's CPU and memory

Increase the number of workers in the Cloud Composer environment

Reduce the number of DAGs in the environment

Google CloudProfessional Data Engineer

Domain 2Objective 3

2.3 Deploying and Operationalizing the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 5)

Part of the Ingesting and processing the data domain, which accounts for ~25% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~13–20 in this domain), expect 4–7 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

9concepts

~25%of the exam

Questions 21–25

21foundation · easy
What is the main goal of continuous integration (CI) in the context of data pipeline development?
Select an answer first
22application · medium
Your data engineering team wants to implement CI/CD for their data pipelines. They currently have a manual process for deploying DAGs to Cloud Composer. They want to automate the deployment and also ensure that the DAGs are syntactically correct before deployment. Which CI/CD practice should they implement?
Select an answer first
23foundation · easy
What is the primary role of Cloud Composer in a data pipeline architecture?
Select an answer first
24expert · hard
A Cloud Composer environment is experiencing performance issues. DAG runs are taking longer than expected, and some tasks are being queued for a long time. You suspect that the environment is under-provisioned. You need to improve performance without over-provisioning. Which action should you take first?
Select an answer first
25foundation · easy
Which tool is commonly used for Infrastructure as Code on Google Cloud?
Select an answer first

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2.3 Deploying and Operationalizing the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 5)

Questions 21–25

What is the main goal of continuous integration (CI) in the context of data pipeline development?

What is the primary role of Cloud Composer in a data pipeline architecture?

Which tool is commonly used for Infrastructure as Code on Google Cloud?

Keep studying — Ingesting and processing the data