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AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are designing an Azure Cognitive Search solution that indexes customer support tickets. The index must include a field for 'sentiment' that is populated from an AI enrichment pipeline. Which TWO actions are required to achieve this?
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
✓
Add a built-in Sentiment skill to the skillset.
Options A and E are correct. The enrichment pipeline must include a built-in Sentiment skill (A) to analyze sentiment from the support ticket text, and an output field mapping must be configured in the indexer (E) to map the skill's output to the 'sentiment' field in the index. Option B is wrong because a custom skill is not needed as the sentiment skill is built-in. Option C is wrong because the built-in sentiment skill uses Azure AI Language, so a custom skill is unnecessary. Option D is wrong because Collection(Edm.String) is not the correct type for sentiment; a simple string or numeric type would be appropriate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a built-in Sentiment skill to the skillset.
Why this is correct
The sentiment skill generates sentiment scores.
- ✗
Implement a custom skill to normalize sentiment values.
Why it's wrong here
No normalization is needed.
- ✗
Create a custom sentiment analysis skill using Azure AI Language.
Why it's wrong here
A built-in sentiment skill exists and is sufficient.
- ✗
Define a 'sentiment' field in the index with type Collection(Edm.String).
Why it's wrong here
The field definition is needed but not sufficient without mapping.
- ✓
Configure an output field mapping in the indexer to map the sentiment output to the index field.
Why this is correct
Field mappings connect skill outputs to index fields.
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