- A
Switch from integrated gradients to XRAI.
Why wrong: XRAI is for images; may not be applicable to deep learning models in general.
- B
Reduce the number of integral approximation steps.
Fewer steps lower computation; optimal steps can be tuned.
- C
Apply feature attribution to only a random subset of predictions.
Why wrong: This would reduce coverage, not computational cost per request.
- D
Use sampled Shapley instead, as it is always cheaper.
Why wrong: Sampled Shapley can be cheaper but may not preserve quality equally; also not the only option.
Question 5 of 1,000
PMLE Monitoring ML Solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 team uses Vertex AI Explainable AI with integrated gradients for a deep learning model. They want to reduce the computational cost of explanations without significantly reducing explanation quality. Which configuration change should they make?
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
✓
Reduce the number of integral approximation steps.
Integrated gradients approximates Shapley values by integrating gradients along a path. Reducing the number of steps (integral approximation steps) reduces computation, but may reduce quality. A moderate reduction balances cost and quality.
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.
- ✗
Switch from integrated gradients to XRAI.
Why it's wrong here
XRAI is for images; may not be applicable to deep learning models in general.
- ✓
Reduce the number of integral approximation steps.
Why this is correct
Fewer steps lower computation; optimal steps can be tuned.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply feature attribution to only a random subset of predictions.
Why it's wrong here
This would reduce coverage, not computational cost per request.
- ✗
Use sampled Shapley instead, as it is always cheaper.
Why it's wrong here
Sampled Shapley can be cheaper but may not preserve quality equally; also not the only option.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML Solutions — This question tests Monitoring ML Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the number of integral approximation steps. — Integrated gradients approximates Shapley values by integrating gradients along a path. Reducing the number of steps (integral approximation steps) reduces computation, but may reduce quality. A moderate reduction balances cost and quality.
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.
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Last reviewed: Jul 4, 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.
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