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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?
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.
You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?
Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:
Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.
Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.
Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?
Using IAM roles to enable access control in BigQuery is the best approach to ensure that only authorized personnel can query the sensitive customer data. IAM allows you to define granular permissions at the project, dataset, or table level, ensuring that users have only the access they need in accordance with the principle of least privilege. For example, you can assign roles like roles/bigquery.dataViewer to allow read-only access or roles/bigquery.dataEditor for more advanced permissions. This approach provides centralized and manageable access control, which is critical for protecting sensitive data.
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?
Using a sales_region user attribute is the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding an access_filter Explore filter on the region_name dimension that references the sales_region user attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
Using object holds and lifecycle management rules is the most efficient and compliant strategy for this scenario because:
Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.
Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.
Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.
