Google Professional-Data-Engineer Exam With Confidence Using Practice Dumps

The requirements are for a daily scheduled load, ingest, and transformation, and specifically a fully managed, no-code solution.

Ingest (Load): The BigQuery Data Transfer Service (DTS) is the fully managed, serverless, and no-code solution for batch loading files (including CSV from Cloud Storage) into BigQuery on a schedule. This is the "ingest" part.

Transform: After loading the raw data into a staging table using DTS, the transformation can be done using BigQuery SQL. This transformation query can then be automated using a Scheduled Query in BigQuery, which is also a fully managed and no-code feature that runs on a schedule.

Fully Managed & No-Code: Both DTS for Cloud Storage and Scheduled Queries are native BigQuery features that are fully managed and configured through the console without requiring code, directly meeting the constraints.

Correcting other options:

A (Cloud Run + Script): Cloud Run requires writing a custom Python script, which violates the no-code requirement.

C (Dataflow + Apache Beam + Cloud Composer): This is a powerful, highly scalable ETL solution, but it requires writing custom code (Apache Beam) and requires setting up and managing a workflow orchestrator (Cloud Composer/Airflow), which violates both the fully managed (Dataflow is serverless, but the code/pipeline itself is custom and needs maintenance) and no-code requirements.

D (BigQuery pipelines): "BigQuery pipelines" is not a distinct, official product name in the Google Cloud documentation that fulfills a no-code scheduled ETL. The closest product is the combination of DTS and Scheduled Queries, as described in option B.