- A
The skill has a timeout set too short; increase the timeout.
Why wrong: 429 is throttling, not timeout.
- B
The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition.
Reducing batch size decreases calls per execution.
- C
The indexer runs too frequently; increase the indexer schedule interval.
Why wrong: While possible, reducing batch size directly addresses the call rate.
- D
The skill output field mapping is incorrect; fix the mapping.
Why wrong: Incorrect mapping would cause errors, not 429.
Question 105 of 993
Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction 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 have an Azure AI Search solution that indexes customer support tickets. The index includes a 'category' field that should be automatically populated using a custom skill that calls an Azure Machine Learning model. However, the skill fails intermittently with HTTP 429 errors. What is the most likely cause and the best fix?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition.
HTTP 429 errors indicate rate limiting, which occurs when the custom skill sends too many requests to the Azure Machine Learning endpoint in a short period. The skill execution batch size controls how many documents are processed per skill invocation; a large batch size increases call volume and triggers throttling. Reducing the batch size in the skillset definition lowers the request rate, preventing 429 errors.
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.
- ✗
The skill has a timeout set too short; increase the timeout.
Why it's wrong here
429 is throttling, not timeout.
- ✓
The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition.
Why this is correct
Reducing batch size decreases calls per execution.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The indexer runs too frequently; increase the indexer schedule interval.
Why it's wrong here
While possible, reducing batch size directly addresses the call rate.
- ✗
The skill output field mapping is incorrect; fix the mapping.
Why it's wrong here
Incorrect mapping would cause errors, not 429.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse HTTP 429 (rate limiting) with timeout or scheduling issues, leading them to pick options that address frequency or duration rather than request volume per batch.
Detailed technical explanation
How to think about this question
Azure AI Search custom skills invoke external endpoints via HTTP requests, and the batch size parameter in the skillset definition determines how many records are sent per call. When the batch size is too large, the endpoint may enforce rate limits (e.g., Azure ML endpoints typically allow 100 requests per minute per deployment). Reducing the batch size spreads requests over more invocations, staying within rate limits. Additionally, you can implement retry policies in the indexer, but the root cause is often batch size.
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.
TExam Day Tips
- 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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The skill execution batch size is too large, causing high call volume; reduce batch size in the skillset definition. — HTTP 429 errors indicate rate limiting, which occurs when the custom skill sends too many requests to the Azure Machine Learning endpoint in a short period. The skill execution batch size controls how many documents are processed per skill invocation; a large batch size increases call volume and triggers throttling. Reducing the batch size in the skillset definition lowers the request rate, preventing 429 errors.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
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Last reviewed: Jul 4, 2026
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