- A
XRAI
XRAI is the method designed for image models in Vertex AI Explainability.
- B
SHAP with KernelExplainer
Why wrong: KernelExplainer is not supported natively in Vertex AI Explainability.
- C
Sampled Shapley
Why wrong: Sampled Shapley is for tabular data, not images.
- D
Integrated Gradients
Why wrong: Integrated Gradients works for images but XRAI is optimized and recommended.
Question 972 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 is using Vertex AI Explainability with a deployed model. They need to generate explanations for image classification predictions. Which explanation method should they configure in the ExplanationSpec?
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
✓
XRAI
XRAI (eXplanation with Ranked Area Integrals) is specifically designed for image models to highlight regions that contribute to the prediction.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
XRAI
Why this is correct
XRAI is the method designed for image models in Vertex AI Explainability.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
SHAP with KernelExplainer
Why it's wrong here
KernelExplainer is not supported natively in Vertex AI Explainability.
- ✗
Sampled Shapley
Why it's wrong here
Sampled Shapley is for tabular data, not images.
- ✗
Integrated Gradients
Why it's wrong here
Integrated Gradients works for images but XRAI is optimized and recommended.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related PMLE OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this PMLE question test?
Monitoring ML Solutions — This question tests Monitoring ML Solutions — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: XRAI — XRAI (eXplanation with Ranked Area Integrals) is specifically designed for image models to highlight regions that contribute to the prediction.
What should I do if I get this PMLE question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related PMLE OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
About these practice questions
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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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