Google Professional Data Engineer Question 153
Select 3Google Cloud PlatformYour organization is building a data lake on Google Cloud to store and process large volumes of structured and unstructured data. The data in the lake will be consumed by various teams for analytics and machine learning workloads. Which considerations should you prioritize to ensure the scalability, security, and effective use of the data lake?
- A
Ensure the data lake uses a scalable storage solution like Cloud Storage and organizes data in a logical, hierarchical structure.
- B
Implement IAM policies and encryption to secure sensitive data stored in the data lake.
- C
Store all data in a single bucket without metadata to simplify access for all users.
- D
Leverage tools like Dataproc, Dataflow, and BigQuery to process and analyze data efficiently within the data lake.
- E
Use Cloud Functions exclusively to manage and process data in the data lake.
Show answer and explanation
Correct answers: A, B, D
Explanation
Building a data lake requires careful planning to ensure scalability, security, and efficient use of resources. Key considerations include using scalable storage solutions like Cloud Storage, securing data with IAM policies and encryption, and leveraging appropriate tools for data processing and analytics. Avoid practices like storing all data in a single location without metadata or relying on a single tool for all operations.
- A. Correct.
This is correct. Using a scalable storage solution like Cloud Storage and organizing data in a structured hierarchy ensures that the data lake can handle large volumes of data and is easy to navigate.
- B. Correct.
This is correct. Security is critical in a data lake. Implementing IAM policies, encryption, and other security measures helps protect sensitive data and ensures compliance with data governance requirements.
- C. Incorrect.
This is incorrect. Storing all data in a single bucket without metadata leads to challenges in managing and retrieving data efficiently, especially as the data lake grows.
- D. Correct.
This is correct. Leveraging processing and analytics tools like Dataproc, Dataflow, and BigQuery ensures that the data lake can support diverse workloads and provide high performance for analytics and machine learning.
- E. Incorrect.
This is incorrect. While Cloud Functions can be useful for event-driven processing, it is not sufficient as the sole tool for managing and processing data in a data lake. Other tools like Dataproc, Dataflow, and BigQuery are more appropriate for large-scale processing and analysis.