Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are using Azure AI Search to index a set of PDF documents. The index includes a 'content' field with the extracted text. Users report that when they search for 'budget forecast', documents containing only 'budget' or 'forecast' are ranked lower than expected. Which configuration change would improve the ranking for multi-word queries?
⚠ Common exam trap
Many candidates confuse boosting (Option D) with semantic understanding, but boosting only increases term frequency weight and does not address the semantic gap between query terms in multi-word searches.
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
✓
Enable semantic search on the index
Semantic search in Azure AI Search uses deep neural network models to understand the intent and context of multi-word queries like 'budget forecast', rather than relying solely on keyword matching. This improves ranking by capturing the semantic relationship between terms, so documents containing both words (or conceptually related content) are ranked higher even if the exact phrase is not present.
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 separate field for each word in the document
Why it's wrong here
That would not improve ranking; it would fragment the content.
- ✗
Change the analyzer to a custom analyzer that splits on spaces only
Why it's wrong here
The default analyzer already handles multi-word queries; the issue is ranking, not tokenization.
- ✓
Enable semantic search on the index
Why this is correct
Semantic search uses advanced ranking models that consider the meaning and relationship between words.
- ✗
Set the 'content' field to a higher boosting value
Why it's wrong here
Boosting only increases the weight of matching documents, but does not address multi-word proximity.
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