Google Professional Data Engineer Question 154
Select 3Google Cloud PlatformYou are designing a data lake solution on Google Cloud for an e-commerce company that collects real-time customer clickstream data and batches of transactional data. The data needs to be ingested, stored, and analyzed efficiently while ensuring scalability, cost-effectiveness, and security. Which of the following are key considerations when implementing this data lake architecture?
- A
Choosing the appropriate storage tier in Cloud Storage based on data access patterns
- B
Using BigQuery for raw data storage to improve query performance
- C
Implementing fine-grained IAM permissions on Cloud Storage buckets
- D
Designing a data lifecycle management policy for archival and deletion of stale data
- E
Setting up a fixed schema for all data ingested into the data lake to ensure consistency
Show answer and explanation
Correct answers: A, C, D
Explanation
When implementing a data lake on Google Cloud, it is important to consider cost optimization (e.g., by selecting the right Cloud Storage tier), security (e.g., implementing fine-grained IAM permissions), and efficient data management (e.g., using data lifecycle policies). BigQuery is not suitable for raw data storage in a data lake, and enforcing a fixed schema can limit the flexibility of a data lake to handle diverse data types.
- A. Correct.
Choosing the appropriate storage tier in Cloud Storage is essential to optimize costs while meeting data access requirements. Frequently accessed data can be stored in the Standard Storage tier, while infrequently accessed data can be moved to Nearline or Coldline Storage.
- B. Incorrect.
BigQuery is a data warehouse solution, not a data lake storage solution. Raw data in a data lake is typically stored in Cloud Storage due to its scalability and flexibility. BigQuery is better suited for querying and analyzing processed data.
- C. Correct.
Fine-grained IAM permissions on Cloud Storage buckets are critical for ensuring security and controlled access to different parts of the data lake.
- D. Correct.
Data lifecycle management policies help automate the archival or deletion of stale data, reducing storage costs and maintaining an efficient data lake.
- E. Incorrect.
A fixed schema is not always required in a data lake as it is designed to handle both structured and unstructured data. Enforcing a fixed schema could reduce the flexibility that data lakes are known for.