Online Access Free Associate-Data-Practitioner Exam Questions

Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

• A.Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
• B.Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
• C.Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
• D.Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.

You are responsible for managing Cloud Storage buckets for a research company. Your company has well- defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

• A.Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
• B.Configure the buckets to use the Standard storage class and enable Object Versioning.
• C.Configure the buckets to use the Archive storage class.
• D.Configure the buckets to use the Autoclass feature.

Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?

• A.Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
• B.Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
• C.Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
• D.Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

• A.Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
• B.Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
• C.Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
• D.Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data. What should you do?

• A.Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
• B.Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
• C.Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
• D.Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.