AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You have an Azure AI Search indexer that uses a custom skill hosted in an Azure Function to normalize product codes. The function occasionally returns HTTP 429 responses. You need the indexer to retry these calls automatically without failing the entire indexing run. What should you configure?
⚠ Common exam trap
The trap here is looking for a retryPolicy property on the custom skill, which Azure AI Search does not support; retries must be handled inside the skill code.
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
✓
Implement retry logic inside the Azure Function and return a success response after retries.
Azure AI Search does not provide a retryPolicy on custom skills. When a custom skill returns transient errors such as HTTP 429, the correct pattern is to implement retry logic inside the skill implementation, for example using exponential backoff in the Azure Function, so the indexer receives a successful response once the downstream call succeeds.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the batchSize property on the indexer to a smaller value.
Why it's wrong here
batchSize controls how many documents are read from the data source and sent through the enrichment pipeline at once. Reducing it lowers overall throughput and may reduce load, but it does not implement retry logic for individual skill calls. The requirement is automatic retry on transient throttling responses, which batchSize does not provide.
- ✓
Implement retry logic inside the Azure Function and return a success response after retries.
Why this is correct
Because the throttling originates from the custom skill's downstream dependency, the function itself should handle transient failures using retry policies such as exponential backoff. Returning a successful response after internal retries prevents the indexer from seeing 429 errors. This is the supported and reliable way to make custom skills resilient to intermittent throttling.
- ✗
Add a retryPolicy to the custom skill definition in the skillset.
Why it's wrong here
Azure AI Search skillsets do not expose a retryPolicy property on skill definitions. While the indexer has some built-in resilience, explicit retry configuration for custom skills is not set this way. Adding an unsupported property causes skillset validation to fail rather than enabling retries.
- ✗
Configure the indexer's maxFailedItems and maxFailedItemsPerBatch to tolerate failures.
Why it's wrong here
maxFailedItems and maxFailedItemsPerBatch determine how many document failures the indexer tolerates before stopping. They allow an indexing run to continue past errors but do not retry the failed skill calls. Using them here would skip documents rather than resolve the transient throttling, leaving product codes unnormalized.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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