- A
Use preemptible VMs for the batch prediction job.
Preemptible VMs are significantly cheaper and suitable for batch jobs.
- B
Use a larger machine type to reduce the number of workers.
Why wrong: Larger machines cost more per worker; may increase total cost.
- C
Use custom machine types with only the necessary resources (vCPU and memory).
Custom machines avoid overprovisioning and reduce cost.
- D
Use TPUs instead of GPUs to accelerate processing.
Why wrong: TPUs are expensive and may not provide cost benefit for all models.
- E
Tune the batch size to maximize throughput per worker.
Optimal batch size improves resource utilization and reduces cost.
Quick Answer
The answer is to use preemptible VMs, tune the batch size to maximize throughput per worker, and select custom machine types to avoid overprovisioning. These three actions directly target batch prediction cost reduction by leveraging cheaper compute resources, optimizing data processing efficiency, and eliminating wasted capacity. Preemptible VMs offer substantial discounts for fault-tolerant workloads, while tuning the batch size balances memory usage and I/O to keep workers fully utilized without idle time. Custom machine types let you match CPU and memory precisely to your model’s needs, avoiding the expense of larger default machines. On the Google Professional Machine Learning Engineer exam, this question tests your ability to optimize cost-performance trade-offs in production pipelines—a common scenario where candidates mistakenly choose larger machines or TPUs, which increase cost without proportional throughput gains. Remember the mnemonic “P-T-C”: Preemptible, Tune batch, Custom type.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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 runs batch predictions on a large dataset using Vertex AI Batch Prediction. They want to reduce costs without significantly increasing processing time. Which three actions should they take? (Choose three.)
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
Use preemptible VMs for the batch prediction job.
Options A, C, and E are correct. A uses preemptible VMs which are cheaper. C tunes batch size to maximize throughput per worker. E uses custom machine types to avoid overprovisioning. Option B increases machine size which may increase cost per worker. Option D uses TPUs which are more expensive and may not be beneficial for all model types.
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 preemptible VMs for the batch prediction job.
Why this is correct
Preemptible VMs are significantly cheaper and suitable for batch jobs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger machine type to reduce the number of workers.
Why it's wrong here
Larger machines cost more per worker; may increase total cost.
- ✓
Use custom machine types with only the necessary resources (vCPU and memory).
Why this is correct
Custom machines avoid overprovisioning and reduce cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use TPUs instead of GPUs to accelerate processing.
Why it's wrong here
TPUs are expensive and may not provide cost benefit for all models.
- ✓
Tune the batch size to maximize throughput per worker.
Why this is correct
Optimal batch size improves resource utilization and reduces cost.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Serving and scaling models — study guide chapter
Learn the concepts, then practise the questions
- →
Serving and scaling models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
506 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Scaling prototypes into ML models practice questions
Practise PMLE questions linked to Scaling prototypes into ML models.
Automating and orchestrating ML pipelines practice questions
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Collaborating within and across teams to manage data and models practice questions
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Architecting low-code ML solutions practice questions
Practise PMLE questions linked to Architecting low-code ML solutions.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Serving and scaling models practice questions
Practise PMLE questions linked to Serving and scaling models.
Monitoring ML solutions practice questions
Practise PMLE questions linked to Monitoring ML solutions.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use preemptible VMs for the batch prediction job. — Options A, C, and E are correct. A uses preemptible VMs which are cheaper. C tunes batch size to maximize throughput per worker. E uses custom machine types to avoid overprovisioning. Option B increases machine size which may increase cost per worker. Option D uses TPUs which are more expensive and may not be beneficial for all model types.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
Last reviewed: Jun 24, 2026
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.