Associate-Data-Practitioner Exam Dumps - Google Cloud Platform Questions and 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.

Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.

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.

Setting thetable partition expiration periodto one year using theALTER TABLEstatement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.

Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery" (https://cloud.google.com/bigquery/docs/partitioned-tables#expiration ):"Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."

UsingCloud Data Fusionis the best solution for this scenario because:

Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.

Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.

Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.

It's a fully managed, cloud-native data integration service for building ETL/ELT data pipelines visually.  

It offers built-in transformations and connectors, including those suitable for data masking and de-identification.  

It provides a standardized, visual interface, making it easier to create and manage data pipelines across various data sources.  

It's designed for data integration and transformation, making it ideal for this scenario.  

It helps to achieve a standardized managed solution.

To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to useDataflowandBigQuery.Dataflowis a fully managed service for real-time data processing and transformation, whileBigQueryis a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.