The answer is to use Dataproc Serverless Spark. This is the correct choice because it eliminates the need to manage a cluster, automatically scaling resources to match the demand of each hourly Spark job and charging only for the resources consumed during execution, which directly reduces costs compared to maintaining a traditional cluster with preemptible workers that still incurs idle time and management overhead. On the Google Professional Data Engineer exam, this scenario tests your understanding of serverless versus cluster-based compute models, often appearing as a trap where candidates mistakenly optimize preemptible VMs instead of eliminating the cluster entirely. The key insight is that for predictable, recurring batch workloads like GPS geohash aggregation, serverless removes both the cost of idle nodes and the complexity of preemptible worker restarts. Memory tip: if you see “hourly batch” and “cost reduction,” think “serverless skips the cluster mess.”
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company processes large volumes of GPS sensor data stored in Cloud Storage. Each hour, they run an Apache Spark job that aggregates the data by geohash region. The job must be cost-effective and scale automatically. Currently, they are using a Dataproc cluster with preemptible workers. Which improvement would best reduce costs while maintaining performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Dataproc Serverless Spark
Dataproc Serverless Spark (Option D) eliminates the need to manage a cluster, automatically scaling resources to match job demand and charging only for the resources consumed during execution. This removes the overhead of preemptible worker management and idle cluster costs, directly reducing expenses while maintaining performance for the hourly aggregation job.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use a larger Dataproc cluster with standard workers
Why it's wrong here
Standard workers are more expensive than preemptible, increasing cost.
✗
Migrate the job to BigQuery scheduled queries
Why it's wrong here
Rewriting Spark SQL to BigQuery SQL may not support all functions and requires data movement.
✗
Switch to Dataflow batch pipeline with Apache Beam
Why it's wrong here
Convert Spark code to Beam requires significant effort, and Dataflow may not be more cost-effective than Serverless Spark.
✓
Use Dataproc Serverless Spark
Why this is correct
Dataproc Serverless Spark runs Spark jobs without cluster management, scales automatically, and you pay only for resources used, reducing cost.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that migrating to a different processing engine (like Dataflow or BigQuery) is always the best cost-saving move, when in fact reusing existing Spark code on a serverless platform avoids migration costs and leverages the same API.
Detailed technical explanation
How to think about this question
Dataproc Serverless Spark uses an ephemeral, auto-scaling cluster that provisions only the necessary executors based on the job's stages, leveraging the Spark ResourceManager's dynamic allocation. Under the hood, it integrates with Cloud Storage via the gs:// connector and uses shuffle-optimized storage, reducing disk I/O costs compared to preemptible workers that may be reclaimed mid-job. In a real-world scenario, if the GPS data volume spikes (e.g., during a holiday), Dataproc Serverless automatically scales up without manual intervention, while preemptible workers might cause job failures due to preemption, requiring retries and increasing costs.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Dataproc Serverless Spark — Dataproc Serverless Spark (Option D) eliminates the need to manage a cluster, automatically scaling resources to match job demand and charging only for the resources consumed during execution. This removes the overhead of preemptible worker management and idle cluster costs, directly reducing expenses while maintaining performance for the hourly aggregation job.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated daily and each file is about 10 GB. The data is not time-sensitive and can be processed within a 24-hour window. Which service is most cost-effective for this use case?
easy
✓ A.Dataproc Serverless with PySpark
B.Dataflow with batch mode
C.Cloud Data Fusion
D.BigQuery Data Transfer Service
Why A: Dataproc Serverless with PySpark is the most cost-effective choice because it eliminates cluster management overhead and automatically scales resources based on workload, charging only for the processing time used. For 10 GB CSV files processed daily within a 24-hour window, the serverless model avoids the fixed costs of a persistent cluster, making it ideal for batch, non-time-sensitive jobs. PySpark's native support for CSV parsing and BigQuery integration via the Spark BigQuery connector ensures efficient data loading without additional services.
Last reviewed: Jun 30, 2026
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