Google Associate-Data-Practitioner Value Pack (Frequently Bought Together)

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

• A. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
• B. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
• C. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
• D. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

• A. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
• B. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
• C. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
• D. Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.

Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Standard storage.
• B. Store the data in Cloud Storage using Archive storage.
• C. Store the data in Cloud Storaqe usinq Nearline storaqe.
• D. Store the data in Cloud Storaqe usinq Coldline storaqe.

You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

• A. Use the gcloud storage command to transfer the data over the internet.
• B. Use Cloud Client Libraries to transfer the data over the internet.
• C. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
• D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

• A. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
• B. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
• C. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
• D. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.