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You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

• A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
• B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
• C. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
• D. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

Your organization needs to store historical customer order dat
a. 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 Nearline storage.
• B. Store the data in Cloud Storage using Archive storage.
• C. Store the data in Cloud Storage using Coldline storage.
• D. Store the data in Cloud Storage using Standard storage.

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 DATE_DIFF() function.
• B. 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.
• C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
• D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

• A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
• B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
• C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
• D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

• A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
• C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
• D. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.