Google Professional Data Engineer exam dumps

Google Professional Data Engineer practice question 37 of 279

Professional Data Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Data Engineer Question 37

Select 3Google Cloud Platform

You are designing a data processing solution for a retail company that needs to run on both Google Cloud and an on-premises environment. One of the key requirements is to ensure the solution is flexible and portable across platforms. Which design considerations should you prioritize to meet this requirement?

  1. A

    Use containerized workloads managed by Kubernetes for deployment.

  2. B

    Leverage proprietary Google Cloud APIs for data processing to optimize performance.

  3. C

    Adopt open-source tools and frameworks such as Apache Beam for data pipelines.

  4. D

    Store data in Google BigQuery to maximize integration with Google Cloud services.

  5. E

    Implement an abstraction layer for database access to decouple the application from specific database technologies.

Show answer and explanation

Correct answers: A, C, E

Explanation

Designing for flexibility and portability requires avoiding vendor lock-in and prioritizing platform-agnostic technologies. Containerization (e.g., Kubernetes) and open-source frameworks (e.g., Apache Beam) enable portability, while abstraction layers allow the decoupling of application logic from specific technologies, ensuring the solution can run seamlessly across cloud and on-premises environments.

  • A. Correct.

    Using containerized workloads managed by Kubernetes ensures portability across cloud providers and on-premises environments, as Kubernetes is platform-agnostic.

  • B. Incorrect.

    Leaning on proprietary Google Cloud APIs can lock the solution into a single cloud provider, reducing portability.

  • C. Correct.

    Adopting open-source tools and frameworks like Apache Beam enables you to run data pipelines on multiple environments, including both cloud and on-premises systems.

  • D. Incorrect.

    While Google BigQuery is a powerful tool, it is specific to Google Cloud and does not support portability across platforms.

  • E. Correct.

    Implementing an abstraction layer decouples the application from specific database technologies, making it easier to port the application across different platforms.

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