Associate-Data-Practitioner Exam Questions Tutorials

Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.

Extract from Google Documentation: From "Object Lifecycle Management" ( ):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."

Comprehensive and Detailed In-Depth Explanation:

A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.

Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging—more effort than necessary for a one-time task.

Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting—far too complex for a simple load.

Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.table file.csv). It’s pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.