Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
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?
⚠ Common exam trap
Candidates often confuse scaling the Azure Function (Option D) as a solution for rate limiting, when in fact it increases the problem, or they assume skipping errors (Option C) is acceptable, missing the requirement to not lose data.
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
✓
Configure the custom skill to execute in batch mode and set a retry policy on the indexer
Configuring the custom skill to execute in batch mode reduces the number of HTTP requests to the third-party API by processing multiple documents per invocation, while setting a retry policy on the indexer ensures that failed documents due to transient HTTP 429 errors are automatically retried. This combination prevents data loss by not skipping documents and by handling rate-limiting gracefully.
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.
- ✗
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.
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