The correct configuration is to enable semantic search and configure a semantic configuration. This works because semantic search leverages deep neural networks to interpret query intent and context, moving beyond simple keyword matching to return results that are conceptually similar even when the wording differs. By defining a semantic configuration, you specify which fields—such as title, content, and category—are used for summarization and ranking, directly enabling meaning-based matching for your customer support ticket index. On the AI-102 exam, this scenario tests your understanding of when to use semantic search over standard full-text or hybrid search; a common trap is confusing semantic configuration with scoring profiles or synonym maps, which handle ranking or term expansion but not deep semantic understanding. Remember the mnemonic: “Semantic search for meaning, not matching”—if the requirement is about understanding intent despite different phrasing, semantic search with a configured profile is the only path.
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 Azure AI Search index stores customer support tickets. You need to implement a search feature that returns semantically similar results even if the query uses different wording. Which configuration should you enable?
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 and configure a semantic configuration
Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than 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.
✗
Use simple query parsing with searchMode=any
Why it's wrong here
Simple parsing does not provide semantic understanding.
✗
Add a synonym map with custom entries
Why it's wrong here
Synonym maps require manual definition and don't capture all semantic variations.
✓
Enable semantic search and configure a semantic configuration
Why this is correct
Semantic search uses AI to understand intent and return conceptually relevant results.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable fuzzy search on the index
Why it's wrong here
Fuzzy search only handles approximate string matching, not semantic similarity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse synonym maps (which handle predefined word equivalence) with semantic search (which handles contextual meaning), leading them to choose synonym maps when the question explicitly requires handling of different wording beyond simple synonyms.
Trap categories for this question
Similar concept trap
Fuzzy search only handles approximate string matching, not semantic similarity.
Detailed technical explanation
How to think about this question
Semantic search in Azure AI Search leverages transformer-based models (e.g., Microsoft's Turing models) to re-rank search results based on semantic relevance, using a semantic configuration that specifies which fields are used for 'title', 'content', and 'keywords' to generate captions and answers. Under the hood, the search engine first performs a BM25-based retrieval to get candidate documents, then applies the semantic reranker to order them by meaning similarity, which is critical for support ticket scenarios where users describe issues with different terminology.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable semantic search and configure a semantic configuration — Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than 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.
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