Objective 3.1
ADPDesign and implement simple data pipelines
Objective 3.1 sits in Data Pipeline Orchestration, which carries 18% of the Associate Data Practitioner exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.
Objective title verbatim from the official objectives. Google Cloud exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
A team migrating existing Apache Spark and Hadoop jobs wants a managed service to run them with minimal rewriting. Which service fits?
Correct.
Concept
Lift-and-shift of Spark or Hadoop workloads calls for a managed cluster service compatible with those engines. Matching the engine avoids rewriting existing jobs.
Why A
Dataproc runs managed Spark and Hadoop clusters that start, scale, and shut down quickly.
Source
Google Cloud, Managed Service for Apache Spark clusters overview, checked August 2026Managed Service for Apache Spark clusters are quick to start, scale, and shutdown, with each of these operations taking 90 seconds or less, on average.
Now you: objective 3.1 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
A pipeline needs a transformation engine, and the team already has deep SQL skills but no Spark experience and wants to work inside BigQuery. Which tool best matches?
Sample question 2 of 3
Which statement best distinguishes ETL from ELT?
Sample question 3 of 3
A team wants ephemeral clusters that start, scale, and shut down in about 90 seconds so they pay only during job runs. Which service provides this?
That’s 3 of the full Associate Data Practitioner bank.
Keep going free: 10 questions per certification in bank practice, with no account.
Continue practisingRead the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.