Google Associate-Data-Practitioner Dumps Questions Answers

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.

Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.