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

Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

• A. Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.
• B. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.
• C. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.
• D. Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

• A. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
• B. Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.
• C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
• D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

• A. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.
• B. Set the dataset-level retention policy in BigQuery to seven years.
• C. Set the table-level retention policy in BigQuery to seven years.
• D. Create a partition by transaction date, and set the partition expiration policy to seven years.

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. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
• B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
• C. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
• D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;