PDE Ingesting and Processing the Data Practice Question
A company uses Google Ads and wants to automatically load their advertising data into BigQuery daily. They also need to transform the data with SQL and schedule a recurring query. Which combination of services meets these requirements with minimal operational overhead?
⚠ Common exam trap
PDE often tests whether candidates choose the fully managed native connector (BigQuery DTS) over custom code (Cloud Functions/Composer) when the question emphasizes 'minimal operational overhead.'
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 for Google Ads and scheduled queries
BigQuery Data Transfer Service has a built-in Google Ads connector that automatically loads advertising data into BigQuery on a schedule, and BigQuery scheduled queries let you transform that data with SQL on a recurring basis — both fully managed with no infrastructure. This combination meets the daily load and SQL transformation requirements with minimal operational overhead.
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 Functions triggered by Cloud Scheduler to call Google Ads API and load into BigQuery
Why it's wrong here
This option introduces substantial operational overhead by requiring custom code within Cloud Functions to manage Google Ads API interaction, authentication, pagination, and data loading into BigQuery. This contradicts the requirement for minimal operational overhead. While Cloud Functions are excellent for scheduled, serverless execution of custom logic, they are best suited for bespoke integrations, real-time event processing, or when pre-built connectors lack specific functionality needed for complex data manipulation before ingestion.
- ✗
Cloud Composer to extract Google Ads API and Dataflow to transform
Why it's wrong here
Cloud Composer and Dataflow require provisioning and managing clusters, contradicting the minimal-operational-overhead requirement. They are tempting because Composer orchestrates complex pipelines and Dataflow scales transformations, but the Google Ads connector with scheduled queries already covers extraction and SQL transformation serverlessly.
- ✗
Storage Transfer Service to move CSV files to GCS, then load into BigQuery
Why it's wrong here
Storage Transfer Service moves files between storage systems and cannot extract Google Ads data, so the daily automated load is unmet. It is tempting because it handles GCS transfers well, but the scenario needs the Google Ads connector plus scheduled SQL transformation, not file movement.
- ✓
BigQuery Data Transfer Service for Google Ads and scheduled queries
Why this is correct
BigQuery Data Transfer Service natively ingests Google Ads data on a daily schedule, and BigQuery scheduled queries run the SQL transformations recurringly. Together they satisfy the daily load, SQL transformation, and scheduling requirements with no servers or orchestration code to maintain.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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