- A
Ensure the index includes a semantic configuration with title and content fields
Semantic configuration is required for semantic ranking to work.
- B
Increase the number of partitions to handle more data
Why wrong: Partitions affect indexing, not relevance.
- C
Configure a scoring profile with boosting based on metadata
Why wrong: Metadata boosting is basic and does not provide context.
- D
Increase the number of replicas to improve query performance
Why wrong: Replicas affect throughput, not relevance.
Question 513 of 993
Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your knowledge mining solution uses Azure AI Search. Users complain that search results are not relevant. You have enabled semantic search but results still lack context. What should you do to improve 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
✓
Ensure the index includes a semantic configuration with title and content fields
Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.
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.
- ✓
Ensure the index includes a semantic configuration with title and content fields
Why this is correct
Semantic configuration is required for semantic ranking to work.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of partitions to handle more data
Why it's wrong here
Partitions affect indexing, not relevance.
- ✗
Configure a scoring profile with boosting based on metadata
Why it's wrong here
Metadata boosting is basic and does not provide context.
- ✗
Increase the number of replicas to improve query performance
Why it's wrong here
Replicas affect throughput, not relevance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume enabling the semantic search feature alone is sufficient, but Azure AI Search requires an explicit semantic configuration to map the fields that the semantic model will use for reranking.
Detailed technical explanation
How to think about this question
Semantic search in Azure AI Search uses a transformer-based model (similar to BERT) to re-rank the top results from a standard BM25 retrieval. The semantic configuration must define which fields (e.g., title, content) the model uses to extract key phrases and compute semantic similarity. Without this configuration, the semantic reranker has no field context to work with, so it falls back to lexical scoring, negating the benefit of enabling the feature.
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: Ensure the index includes a semantic configuration with title and content fields — Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.
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.
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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 →
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Last reviewed: Jul 4, 2026
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