Google Professional Data Engineer Question 217
Select 3Google Cloud PlatformYou are a Data Engineer tasked with conducting data discovery for a new project. The project involves analyzing customer transaction data stored across multiple Google Cloud Storage buckets. You need to identify the schema, data quality issues, and relationships between datasets to ensure the data is ready for downstream processing. Which of the following steps should you take to effectively perform data discovery in this scenario?
- A
Use Data Catalog to create metadata tags and perform schema exploration.
- B
Leverage BigQuery to load the data and run SQL queries to identify data patterns and quality issues.
- C
Manually download all data from the Cloud Storage buckets and analyze it using local tools like Excel.
- D
Use Dataprep to visually inspect the data, clean it, and identify relationships between datasets.
- E
Enable Cloud Monitoring to track the performance of data pipelines.
Show answer and explanation
Correct answers: A, B, D
Explanation
Effective data discovery in Google Cloud involves leveraging tools like Data Catalog for metadata management, BigQuery for querying large datasets, and Dataprep for data cleaning and relationship analysis. These tools are designed to handle large-scale cloud data efficiently, unlike manual processes or tools like Cloud Monitoring, which are not tailored for data discovery.
- A. Correct.
Using Data Catalog allows you to create metadata tags and explore the schema of the data, making it an essential tool for data discovery.
- B. Correct.
BigQuery provides the ability to run SQL queries on large datasets efficiently, making it ideal for identifying data patterns and quality issues during data discovery.
- C. Incorrect.
Manually downloading data and using local tools like Excel is inefficient and not scalable for large datasets typically encountered in cloud environments.
- D. Correct.
Dataprep is a powerful tool for visually inspecting, cleaning, and understanding relationships between datasets, which is critical for data discovery.
- E. Incorrect.
Cloud Monitoring is primarily used for tracking the performance of systems and pipelines, not for data discovery tasks.