AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are using Azure AI Search to build a knowledge base for a customer support portal. The index includes a 'sentiment' field that should be populated using the Sentiment skill. However, the sentiment scores are not being written to the index. The skillset runs successfully. What is the most likely cause?
⚠ Common exam trap
A common mix-up: candidates assume a successful skillset execution guarantees data is written to the index, but Azure AI Search requires explicit output field mappings in the indexer to bridge skill outputs to index fields, and this step is often overlooked.
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 output field mapping for 'sentiment' is missing or incorrectly defined in the indexer.
The Sentiment skill outputs a 'double' value for sentiment score, but the indexer requires an explicit output field mapping to write that value into the index's 'sentiment' field. Even when a skillset runs successfully, without a correct output field mapping in the indexer definition, the skill's output is not transferred to the index. The indexer's field mappings control how enriched data flows from the skillset's output nodes to the index fields.
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 output field mapping for 'sentiment' is missing or incorrectly defined in the indexer.
Why this is correct
Skillset execution writes enriched values into the enrichment tree, not the index. Without an output field mapping linking the sentiment skill output to the target index field, scores are discarded, so the index remains empty despite successful skillset execution.
- ✗
The Sentiment skill is not correctly configured in the skillset.
Why it's wrong here
A misconfigured skill would typically fail validation or produce errors during skillset execution, but the stem confirms the skillset runs successfully. This would be the cause when the skill is absent, disabled, or missing its input source, rather than when output simply fails to reach the index.
- ✗
The indexer is in a failed state and not processing documents.
Why it's wrong here
A failed indexer would halt document processing entirely and report an error status, contradicting the stem's statement that the skillset runs successfully. This would be the cause when no documents reach the enrichment pipeline at all, which is not the scenario described.
- ✗
The sentiment field in the index is of type 'Collection(Edm.String)' but the skill outputs a double.
Why it's wrong here
A type mismatch would surface as a skillset or indexer error, yet the stem states the skillset runs successfully. Collection(Edm.String) is correct for storing multiple sentiment labels, and the skill's double output maps to Edm.Double, so the field type is not the cause.
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