Google Professional Data Engineer Question 87
Select 3Google Cloud PlatformYou are a data engineer working on a Google Cloud project. Your team needs to cleanse a large dataset stored in BigQuery to remove invalid entries, standardize data formats, and handle missing values. Which of the following approaches would be the MOST effective and scalable for this task?
- A
Use BigQuery SQL functions such as SAFE_CAST, IFNULL, and REGEXP_REPLACE to cleanse the data directly within BigQuery.
- B
Export the dataset to a CSV file, manually cleanse the data using spreadsheet software, and re-upload it to BigQuery.
- C
Leverage Dataflow with an Apache Beam pipeline to preprocess and cleanse the data before writing it back to BigQuery.
- D
Use Dataprep by Trifacta to create a cleansing workflow and apply it to the BigQuery dataset.
- E
Write a Python script to process the dataset using Pandas on a local machine and upload the cleaned data back to BigQuery.
Show answer and explanation
Correct answers: A, C, D
Explanation
Data cleansing is a critical step in ensuring data quality and reliability for downstream analytics. In Google Cloud, using tools like BigQuery SQL functions, Dataflow, or Dataprep provides scalable and efficient ways to handle large datasets. While manual methods or local processing might work for small datasets, they are unsuitable for enterprise-scale operations. BigQuery SQL functions are ideal for direct cleansing within the data warehouse, Dataflow is excellent for complex preprocessing pipelines, and Dataprep provides an interactive and user-friendly interface for data cleansing tasks.
- A. Correct.
This is correct. BigQuery provides built-in SQL functions that are highly efficient and scalable for cleansing operations directly within the data warehouse. Functions like SAFE_CAST can handle type conversions safely, IFNULL can manage missing values, and REGEXP_REPLACE is useful for standardizing formats.
- B. Incorrect.
This is incorrect. Exporting and manually cleansing data in a spreadsheet is not scalable for large datasets and increases the risk of human error.
- C. Correct.
This is correct. Dataflow, with its Apache Beam framework, is a highly scalable solution for preprocessing and cleansing large datasets. It is particularly useful for complex transformations and automated workflows.
- D. Correct.
This is correct. Dataprep by Trifacta is a cloud-native solution specifically designed for interactive data cleansing and transformation workflows. It integrates seamlessly with BigQuery.
- E. Incorrect.
This is incorrect. While Pandas is a powerful library for data manipulation, processing large datasets locally is not scalable and may lead to performance bottlenecks. Additionally, it introduces complexity in handling data transfers.