Google Associate Data Practitioner Practice Test - Questions Answers, Page 4

Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.

Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.

Setting the table partition expiration period to one year using the ALTER TABLE statement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.

Dataform workflows are the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

Scheduling the creation of a snapshot of the table weekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery's time travel feature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.

Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.

Using Dataflow to create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.