Implement knowledge mining and information extraction solutions →easyMultiple SelectObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Which TWO capabilities are available in Azure AI Search to improve search relevance? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse features that expand query scope (like synonym maps or filters) with features that directly alter relevance scoring or ranking, leading them to pick options that affect recall rather than relevance.
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
✓
Scoring profiles
Scoring profiles allow you to boost search results based on specific criteria such as field weight, freshness, or geographic distance, directly influencing relevance. Semantic ranking uses deep neural networks to re-rank results based on the semantic meaning of the query and documents, improving relevance beyond simple keyword matching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Filters
Why it's wrong here
Filters restrict results, not improve ranking.
- ✗
Indexers
Why it's wrong here
Indexers import data, not improve relevance.
- ✓
Scoring profiles
Why this is correct
Scoring profiles boost results based on criteria.
- ✓
Semantic ranking
Why this is correct
Semantic ranking re-ranks results for relevance.
- ✗
Synonym maps
Why it's wrong here
Synonym maps improve recall, not relevance.
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Same concept, more angles
2 more ways this is tested on AI-102
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your company uses Azure AI Search for an internal knowledge base. Users complain that searches for 'annual report 2023' return irrelevant results. You analyze the search index and find that the content field contains large blocks of text from PDFs. You need to improve relevance without re-indexing all documents. Which approach should you take?
hard- A.Enable spell correction in the search query
- B.Add a custom scoring profile based on term frequency
- C.Change the index analyzer to a different language
- ✓ D.Enable semantic ranking on the search index
Why D: Semantic ranking re-ranks search results using deep learning models to understand the intent and context of the query, rather than just keyword matching. Since the content field contains large text blocks from PDFs, semantic ranking can extract the most relevant passages and improve result relevance without requiring re-indexing or modifying the existing index schema.
Variation 2. An organization uses Azure AI Search to power an internal knowledge base. They notice that search results are returning irrelevant documents. The index includes a 'content' field with full text and a 'tags' field with metadata. Users often search for specific terms that appear in the 'tags' field. How should you configure the search index to improve relevance?
hard- A.Add a custom scoring profile based on freshness.
- ✓ B.Configure a scoring profile with a higher weight for the 'tags' field.
- C.Set the 'tags' field to use the 'keyword' analyzer.
- D.Enable semantic search on the 'content' field.
Why B: Configuring a scoring profile with a higher weight for the 'tags' field increases the relevance score of documents where search terms match the tags, thereby prioritizing those results. Option A (freshness-based scoring) would favor newer documents but does not address matching on tags. Option C sets the 'tags' field to use the 'keyword' analyzer, which changes tokenization but does not adjust field weighting. Option D enables semantic search on the 'content' field, which enhances understanding of natural language queries but does not specifically boost the weight of the tags field.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.