PDE Ingesting and Processing the Data Practice Question
You need to process a large volume of event data from Cloud Storage, apply complex transformations using Apache Spark, and then load the results into BigQuery. The data arrives in batches every hour. You want to minimize costs by using preemptible VMs. Which service should you use?
⚠ Common exam trap
PDE often tests the confusion between Dataflow and Dataproc, where candidates pick Dataflow for Spark workloads, but Dataflow uses Apache Beam, not Spark, and does not support preemptible VMs in the same cost-optimized way.
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
✓
Dataproc
Dataproc is a managed Apache Spark and Hadoop service that supports preemptible VMs, making it the ideal choice for batch processing with complex transformations using Spark while minimizing costs. It integrates natively with Cloud Storage for input and BigQuery for output, and preemptible VMs can reduce compute costs by up to 80%. The hourly batch pattern aligns with Dataproc's job-based execution model.
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 Composer
Why it's wrong here
Cloud Composer orchestrates workflows through Airflow DAGs; it schedules and triggers jobs but does not itself run Apache Spark transformations on preemptible VMs. It is tempting because it coordinates pipelines, and would be correct for scheduling the hourly batch, not for executing the Spark processing itself.
- ✗
BigQuery
Why it's wrong here
BigQuery is a serverless analytics warehouse that runs SQL, not Apache Spark, so it cannot execute the transformation logic or use preemptible VMs. It is tempting because it is the required load destination, and would be correct if the task were querying or storing the transformed results rather than processing them.
- ✓
Dataproc
Why this is correct
Dataproc runs Apache Spark natively and supports preemptible VMs for worker nodes, cutting compute costs substantially. It handles hourly batch ingestion from Cloud Storage, applies the complex Spark transformations, and writes results into BigQuery, matching the cost-minimisation constraint.
- ✗
Dataflow
Why it's wrong here
Dataflow is a managed Apache Beam runner, not a Spark execution environment, so it cannot run Spark transformations directly. It would be correct for streaming or batch pipelines written in Beam, such as reading Pub/Sub and writing to BigQuery.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.