PDE Ingesting and Processing the Data Practice Question
A data engineer needs to ingest daily Salesforce reports into BigQuery without writing custom code. The reports are exported to an Amazon S3 bucket on a schedule. Which service should they use to automate the transfer?
⚠ Common exam trap
Test-takers frequently confuse Cloud Storage Transfer Service (which only moves files between storage buckets) with BigQuery Data Transfer Service (which directly ingests from SaaS applications like Salesforce into BigQuery), leading them to pick option D when the requirement is for a no-code, direct-to-BigQuery solution.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
BigQuery Data Transfer Service
The BigQuery Data Transfer Service (BQDTS) is the correct choice because it supports scheduled, automatic ingestion of Salesforce reports exported to Amazon S3 into BigQuery without requiring custom code. It can connect to S3 as a source and load the data into BigQuery tables on a schedule, handling schema detection and incremental updates. This meets the requirement of no-code automation from an S3 bucket.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Dataproc
Why it's wrong here
Dataproc is a managed Spark/Hadoop service; requires writing and maintaining code to load data.
- ✓
BigQuery Data Transfer Service
Why this is correct
Supports Amazon S3 as a source for scheduled transfers directly into BigQuery.
- ✗
Cloud Composer
Why it's wrong here
Composer is Apache Airflow-based orchestration; can be used but requires custom DAGs, not a turnkey transfer service.
- ✗
Cloud Storage Transfer Service
Why it's wrong here
Storage Transfer Service copies objects between GCS and external clouds, but does not load into BigQuery directly.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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