- A
Filters
Why wrong: Filters restrict results, not improve ranking.
- B
Indexers
Why wrong: Indexers import data, not improve relevance.
- C
Scoring profiles
Scoring profiles boost results based on criteria.
- D
Semantic ranking
Semantic ranking re-ranks results for relevance.
- E
Synonym maps
Why wrong: Synonym maps improve recall, not relevance.
Question 902 of 993
Implement knowledge mining and information extraction solutions →easyMultiple SelectObjective-mapped
Improve Search Relevance with Scoring Profiles and Semantic Ranking
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO capabilities are available in Azure AI Search to improve search relevance? (Choose two.)
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Semantic ranking
Why this is correct
Semantic ranking re-ranks results for relevance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Synonym maps
Why it's wrong here
Synonym maps improve recall, not relevance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Scoring profiles work by computing a final score as a weighted sum of field-level scores, freshness decay functions, or distance functions, allowing fine-tuned relevance tuning per index. Semantic ranking leverages a transformer-based model (similar to BERT) to compute a semantic similarity score between the query and each document, then re-orders the top results from the initial BM25-ranked set, often requiring a semantic configuration and a semantic search tier.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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: Option B is correct because 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.
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Last reviewed: Jul 4, 2026
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