The correct choice is to configure the custom skill to execute in batch mode and set a retry policy on the indexer. This works because when handling HTTP 429 errors in custom skills, the Azure AI Search indexer controls the execution flow—batching documents reduces the number of API calls, while the retry policy automatically reattempts failed batches after a backoff period, ensuring no data is lost during enrichment. On the AI-102 exam, this tests your understanding that custom skills lack built-in retry logic unlike Microsoft’s native skills, so you must explicitly configure resilience at the indexer level. A common trap is assuming scaling the Azure Function alone solves throttling, but the indexer’s batch and retry settings are what govern skillset execution. Memory tip: think “Batch and Backoff”—the indexer batches work and backs off on 429s to keep enrichment flowing.
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. 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.
Your team is implementing a knowledge mining solution using Azure AI Search with custom skills. The custom skill, deployed as an Azure Function, calls a third-party API to enrich documents. You notice that some documents fail enrichment with HTTP 429 (too many requests) errors. You need to ensure all documents are enriched without losing data. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the custom skill to execute in batch mode and set a retry policy on the indexer
Option D is correct because configuring the custom skill to execute in batch mode with a retry policy allows the skillset to handle throttling by retrying failed batches. Option A is wrong because increasing the number of partitions does not affect custom skill execution. Option B is wrong because scaling the Azure Function may help but the skillset execution is controlled by the indexer. Option C is wrong because Azure AI Search manages retries for built-in skills, but custom skills need explicit retry configuration.
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.
✓
Configure the custom skill to execute in batch mode and set a retry policy on the indexer
Why this is correct
Batch mode reduces API calls, and retry policy handles transient failures.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of partitions in the Azure AI Search service
Why it's wrong here
Partitions affect indexing throughput, not custom skill execution.
✗
Enable indexer error handling to skip failed documents
Why it's wrong here
Skipping documents loses data, which is not acceptable.
✗
Scale out the Azure Function to multiple instances
Why it's wrong here
Custom skills run in the context of the indexer; the indexer controls concurrency.
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 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: Configure the custom skill to execute in batch mode and set a retry policy on the indexer — Option D is correct because configuring the custom skill to execute in batch mode with a retry policy allows the skillset to handle throttling by retrying failed batches. Option A is wrong because increasing the number of partitions does not affect custom skill execution. Option B is wrong because scaling the Azure Function may help but the skillset execution is controlled by the indexer. Option C is wrong because Azure AI Search manages retries for built-in skills, but custom skills need explicit retry configuration.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are implementing a knowledge mining solution with Azure AI Search that ingests data from Azure Blob Storage. The pipeline includes a custom skill that calls an external API for specialized entity extraction. The custom skill sometimes returns HTTP 429 (Too Many Requests). How should you handle this to ensure reliable indexing?
hard
A.Reduce the batch size in the indexer
B.Increase the skill timeout
✓ C.Configure a retry policy on the custom skill
D.Schedule the indexer to run less frequently
Why C: Option D is correct because the indexer retry policy can be configured to handle transient failures, including 429 errors, by retrying after a delay. Option A is incorrect because reducing the batch size might not help if the API rate limit is per request. Option B is incorrect because increasing timeouts does not address rate limiting. Option C is incorrect because scheduling the indexer less frequently does not prevent failures during execution.
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Question Discussion
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