Implement knowledge mining and information extraction solutions →easyMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your team has built a knowledge mining pipeline using Azure AI Search and Document Intelligence. After ingestion, you notice that some documents are not appearing in search results. What is the most likely cause?
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 indexer encountered errors and marked the documents as failed
If the indexer encounters errors while processing specific documents (e.g., unsupported formats, parsing failures, or skill errors), those documents are marked as failed and not indexed. This explains why only some documents are missing from search results. Option B is incorrect because semantic configuration affects ranking and relevance features, not whether documents are indexed. Option C is incorrect because insufficient replicas affect query performance and availability, not document ingestion. Option D is incorrect because throttling during high query volume would impact all search queries, not the indexing of specific documents.
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 indexer encountered errors and marked the documents as failed
Why this is correct
Indexer errors prevent documents from being indexed.
- ✗
The index does not have a semantic configuration
Why it's wrong here
Semantic config is optional and does not prevent indexing.
- ✗
The search service has insufficient replicas
Why it's wrong here
Replicas affect query throughput, not indexing.
- ✗
The search service is throttled due to high query volume
Why it's wrong here
Throttling affects queries, not indexing.
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