Associate-Data-Practitioner 試験問題を無料オンラインアクセス

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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
• B.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.
• 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's business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

• A.Modify the schema to use parameterized data types.
• B.Create a scheduled query to calculate and store intermediate results.
• C.Disable BiqQuery query result caching.
• D.Create materialized views.

Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

• A.Enable Object Versioning.
• B.Change the bucket storage class to Archive.
• C.Set temporary object holds.
• D.Set a bucket retention policy.

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

• A.Create a data masking rule.
• B.Create a row-level access policy.
• C.Add a policy tag in BigQuery.
• D.Grant the appropriate 1AM permissions on the dataset.

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

• A.
• B.
• C.
• D.