Google Professional Data Engineer Question 212
Select 3Google Cloud PlatformYou are a Data Engineer working for a retail company. Your team is tasked with analyzing customer purchase patterns to identify trends and improve marketing strategies. The data resides in Google BigQuery and includes millions of rows across multiple tables. You need to explore and analyze the data efficiently. Which of the following actions should you consider to meet your goals?
- A
Use SQL queries with JOINs to combine data from multiple tables for trend analysis.
- B
Export the data from BigQuery to a local machine for analysis using a spreadsheet tool.
- C
Leverage BigQuery's built-in ML capabilities, such as BigQuery ML, to create predictive models directly on the data.
- D
Use Dataflow to preprocess the data before loading it into BigQuery for analysis.
- E
Enable partitioning and clustering on the tables in BigQuery to optimize query performance and cost.
Show answer and explanation
Correct answers: A, C, E
Explanation
To explore and analyze data efficiently in BigQuery, it is important to utilize the platform's built-in capabilities such as SQL for querying, BigQuery ML for machine learning, and table partitioning and clustering for performance optimization. Exporting large datasets to external tools is inefficient and should be avoided unless absolutely necessary. Dataflow is not required for preprocessing data already in BigQuery unless additional transformations are needed.
- A. Correct.
Using SQL queries with JOINs is an efficient way to combine data from multiple tables for analysis directly in BigQuery without transferring data outside the platform.
- B. Incorrect.
Exporting the data to a local machine for analysis is not recommended for large datasets (millions of rows) due to inefficiency, potential performance issues, and the risk of data handling errors.
- C. Correct.
BigQuery ML allows you to build and train machine learning models directly on BigQuery datasets, which is highly efficient for predictive analysis and trend identification.
- D. Incorrect.
While Dataflow is useful for preprocessing streaming or batch data, it is not necessary to preprocess data already stored in BigQuery unless there are specific transformation needs.
- E. Correct.
Enabling partitioning and clustering in BigQuery improves query performance and reduces costs by allowing the system to process only relevant data segments during analysis.