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 needs to serve a model for low-frequency inference requests (a few hundred per month) from multiple regions. The priority is simplicity and minimal cost without maintaining infrastructure. Which serving option should they choose?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Vertex AI Batch Prediction triggered as needed.
Option D is correct because Vertex AI Batch Prediction runs on demand and is cost-effective for infrequent large batches. Option A is wrong because real-time endpoint incurs per-hour cost even if idle. Option B is wrong because Cloud Run is better for online, not offline. Option C is wrong because Dataflow is more complex and designed for streaming.
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.
✗
Deploy a real-time Vertex AI Endpoint with min replicas set to 1.
Why it's wrong here
Real-time endpoint with a replica incurring hourly cost wastes money for low volume.
✗
Set up a Dataflow streaming pipeline to process requests.
Why it's wrong here
Streaming pipeline adds complexity and cost for very low volume.
✓
Use Vertex AI Batch Prediction triggered as needed.
Why this is correct
Batch prediction is serverless, pay-per-query, and ideal for infrequent large predictions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Cloud Run with serving container and scale to zero.
Why it's wrong here
Cloud Run is for online request/response; not ideal for batch and may have cold start for each request if infrequent.
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 — 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 Vertex AI Batch Prediction triggered as needed. — Option D is correct because Vertex AI Batch Prediction runs on demand and is cost-effective for infrequent large batches. Option A is wrong because real-time endpoint incurs per-hour cost even if idle. Option B is wrong because Cloud Run is better for online, not offline. Option C is wrong because Dataflow is more complex and designed for streaming.
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 →
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.