Associate-Data-Practitioner Dumps with Practice Exam Questions Answers

Comprehensive and Detailed In-Depth Explanation:

Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let’s analyze:

Option A: BigQuery ML’s time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.

Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML’s in-place processing.

Option C: Linear regression predicts continuous values but doesn’t handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

Comprehensive and Detailed In-Depth Explanation:

BigQuery uses IAM for access control, adhering to least privilege by granting only necessary permissions.

Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to authorized users, aligning with Google’s security best practices.

Option B: BigQuery doesn’t support SQL GRANT for dataset privileges; access is managed via IAM or authorized views.

Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery’s native controls and adds complexity.

Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.

Creating amaterialized view in BigQuerywith theSUM()function and theDATE_SUB()function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using theDATE_SUB()function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.