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Welcome to Cloud Exam Ready’s Practice Exams for Google Cloud Professional Data Engineer Certification!
Are you ready to conquer the Google Cloud Professional Data Engineer certification and take your career to new heights? Look no further! Cloud Exam Ready has crafted this comprehensive set of practice exams to help you build confidence, bridge knowledge gaps, and ensure you’re fully prepared to ace your certification.
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Realistic Exam Scenarios: Each question is designed to mirror the actual certification exam format, giving you the feel of the real test.
In-Depth Explanations: Gain a deeper understanding of every question with detailed explanations for both correct and incorrect answers, reinforcing your learning.
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SAMPLE QUESTION:
You have deployed multiple data processing tasks in your Cloud Composer 2 setup. Some tasks are failing in Apache Airflow, and an analysis of the monitoring dashboard shows increased worker memory usage with instances of worker pod terminations.
What steps would you take to address these issues? (Select two.)
A. Extend the interval for parsing directed acyclic graph (DAG) files.
B. Upgrade the Cloud Composer 2 environment from medium to large scale.
C. Raise the cap on worker counts and decrease worker concurrency.
D. Allocate more memory resources to the Airflow workers.
E. Allocate additional memory to the Airflow triggerer.
Correct Answers below
Correct Answers:
C. Raise the cap on worker counts and decrease worker concurrency.
D. Allocate more memory resources to the Airflow workers.
Explanation:
C: Raising the cap on worker counts and decreasing worker concurrency can help distribute the load more evenly across workers, preventing excessive memory usage by any single worker and reducing the risk of crashes.
D: Allocating more memory to Airflow workers addresses the root cause of increased memory usage, ensuring stability and preventing worker pod terminations.
Why Other Options Are Incorrect:
A: Extending the interval for parsing DAG files does not directly reduce memory usage or prevent worker pod terminations.
B: Upgrading the environment may not address worker memory issues and could increase costs unnecessarily.
E: Allocating additional memory to the Airflow triggerer is unrelated to the issue, as the problem lies with worker memory.
Category: Maintain and Automate Data Workloads
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