Google Professional Data Engineer Question 218
Select 3Google Cloud PlatformYou are a data engineer at a retail company tasked with conducting data discovery on a new dataset received from the marketing team. The dataset contains customer purchase information and marketing campaign data. Your goal is to understand the structure, quality, and content of the data to determine its usability for building analytics dashboards. Which of the following actions are appropriate steps for conducting data discovery?
- A
Inspect the schema and data types of the dataset to understand its structure.
- B
Run data quality checks, such as identifying null values, duplicates, and outliers.
- C
Immediately load the dataset into the production data warehouse without further validation.
- D
Perform exploratory data analysis (EDA) to identify patterns, distributions, and anomalies.
- E
Assume the dataset is accurate because it comes from the marketing team and skip validation.
Show answer and explanation
Correct answers: A, B, D
Explanation
Conducting data discovery involves thoroughly examining the dataset's structure, quality, and content. This includes inspecting the schema, running data quality checks, and performing exploratory data analysis (EDA). These steps help ensure the data is suitable for its intended purpose. Skipping validation or assuming the dataset is accurate without verification risks introducing errors into downstream processes, while immediately loading unverified data into production is not recommended.
- A. Correct.
Inspecting the schema and data types is critical for understanding the structure of the dataset and ensuring it aligns with your use case.
- B. Correct.
Running data quality checks is essential to identify potential issues, like missing or inconsistent data, that could impact downstream processes.
- C. Incorrect.
Loading the dataset directly into production without validation is risky and considered a bad practice as it could introduce errors into critical systems.
- D. Correct.
Performing exploratory data analysis (EDA) helps uncover useful insights, patterns, and potential issues in the data, which is an important part of data discovery.
- E. Incorrect.
Assuming the dataset is accurate without validation is a poor practice as it ignores potential data quality issues that could adversely affect the project.