The correct answer is to increase the scoringTimeout parameter to 'PT10M'. This resolves the deployment timeout because the default value of PT5M (five minutes) is often insufficient for Custom Vision models deployed to Azure ML endpoints, particularly when inference latency is higher due to model complexity or cold-start scenarios. The scoringTimeout property defines the maximum time the endpoint waits for a model to return a prediction before timing out, and extending it gives the model adequate processing time. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of ARM template configuration for managed endpoints, specifically how to troubleshoot deployment failures by adjusting timeout settings rather than unrelated parameters like authMode, compute type, or model version. A common trap is confusing performance tuning with timeout configuration—changing to GPU might speed inference but does not fix a timeout limit. Memory tip: think “Score needs more seconds” to recall that scoringTimeout controls the prediction window.
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are deploying a Custom Vision model to an Azure Machine Learning managed endpoint using the above ARM template snippet. The deployment fails with a timeout error. Which parameter should you adjust?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase scoringTimeout to 'PT10M'
Option C is correct because the scoringTimeout of 5 minutes (PT5M) may be too short for the model to return predictions. Increasing it allows more time. Option A is wrong because authMode does not affect timeout. Option B is wrong because compute type (CPU) is not the direct cause of timeout; changing to GPU might speed up but not necessarily. Option D is wrong because the model version is not related to timeout.
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.
✗
Change model version to 2
Why it's wrong here
Version change unrelated to timeout.
✗
Change compute to 'GPU'
Why it's wrong here
May improve speed but timeout is the issue.
✗
Change authMode to 'AAD'
Why it's wrong here
Authentication mode doesn't affect timeout.
✓
Increase scoringTimeout to 'PT10M'
Why this is correct
Extends timeout to accommodate slow inference.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 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.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase scoringTimeout to 'PT10M' — Option C is correct because the scoringTimeout of 5 minutes (PT5M) may be too short for the model to return predictions. Increasing it allows more time. Option A is wrong because authMode does not affect timeout. Option B is wrong because compute type (CPU) is not the direct cause of timeout; changing to GPU might speed up but not necessarily. Option D is wrong because the model version is not related to timeout.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 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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Question Discussion
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