The answer is to increase the Dataflow batch size to process more data per worker. This is correct because larger batch sizes allow each Dataflow worker to handle more records per processing cycle, which reduces the total number of workers required and the overall compute time for the same throughput, directly lowering Dataflow’s compute costs without altering the pipeline’s hourly schedule or the AutoML training budget. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of cost optimization within Vertex AI Pipelines, specifically how Dataflow’s batch size tuning impacts resource utilization—a common trap is to mistakenly focus on reducing AutoML training time or changing the pipeline frequency, which would sacrifice throughput or violate business requirements. Remember the memory tip: “Batch big, workers few, cost cuts through.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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 has a pipeline that uses Vertex AI Pipelines to fetch data from BigQuery, preprocess with Dataflow (without code?), then train an AutoML model, and deploy. However, they want to reduce cloud costs. The pipeline runs hourly. Which change will most reduce compute costs while maintaining throughput?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase Dataflow batch size to process more data per worker
Option C is correct because increasing the Dataflow batch size allows each worker to process more data per batch, reducing the number of workers needed and the total compute time for the same throughput. This directly lowers Dataflow's compute cost without affecting the pipeline's hourly schedule or the AutoML training budget.
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.
✗
Decrease the AutoML training budget from 10 to 1 node hour
Why it's wrong here
May degrade model quality.
✗
Replace Dataflow preprocessing with a Cloud Function that runs on each file upload
Why it's wrong here
Cloud Functions have time limits and may not handle large batch processing efficiently.
✓
Increase Dataflow batch size to process more data per worker
Why this is correct
Reduces the number of worker instances needed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch from Vertex AI Pipelines to Cloud Composer for orchestration
Why it's wrong here
Composer also incurs costs and may be more expensive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume reducing AutoML node hours (Option A) is the most direct way to cut costs, but the question specifies 'maintaining throughput' and the pipeline runs hourly, so Dataflow preprocessing is the dominant cost driver, not the model training budget.
Detailed technical explanation
How to think about this question
Dataflow uses autoscaling based on backlog, and increasing batch size (via the `maxBatchSize` parameter or `batchSize` in transforms) reduces the number of worker VMs required to process the same volume of data, lowering total vCPU-hours. In practice, this optimization is critical for pipelines with high data volumes, as Dataflow billing is based on worker-seconds, and batching directly impacts the number of processing steps per worker. A real-world scenario: a pipeline processing 100 GB hourly can reduce worker count from 10 to 4 by doubling batch size, cutting costs by 60% while maintaining latency.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase Dataflow batch size to process more data per worker — Option C is correct because increasing the Dataflow batch size allows each worker to process more data per batch, reducing the number of workers needed and the total compute time for the same throughput. This directly lowers Dataflow's compute cost without affecting the pipeline's hourly schedule or the AutoML training budget.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This PMLE 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 PMLE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.