The answer is that the index lacks a vector configuration with a similarity metric. Azure AI Search requires a defined similarity metric—such as cosine, dotProduct, or euclidean—within the index’s vector configuration to compute relevance scores for vector search results; without it, the search engine cannot rank results by semantic closeness, leading to unsorted or default ordering. On the Microsoft Azure AI Engineer Associate AI-102 exam, this tests your understanding of vector search setup, often appearing as a trap where candidates assume vector fields alone enable relevance sorting, but the similarity metric is the missing link. A common memory tip is to think of the metric as the “ruler” for measuring distance between vectors—without it, you have coordinates but no way to compare them.
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. 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.
Refer to the exhibit. You are implementing an Azure AI Search index for semantic search with vector support. The index includes a field 'descriptionVector' of type Collection(Edm.Single) with 1536 dimensions. When you run a vector search query, you notice that results are not sorted by relevance. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The index does not have a vector configuration with a similarity metric
Option C is correct because Azure AI Search requires a vector configuration with a similarity metric (e.g., cosine, dotProduct, euclidean) to compute relevance scores for vector search results. Without this configuration, the search engine cannot sort results by relevance, leading to unsorted or default ordering. The similarity metric is defined in the index's vector configuration and is essential for ranking vector query results.
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.
✗
The 'descriptionVector' field is not searchable
Why it's wrong here
It is searchable in the exhibit.
✗
The vector dimensions do not match the embedding model output
Why it's wrong here
1536 is a common dimension size; not the cause.
✓
The index does not have a vector configuration with a similarity metric
Why this is correct
Vector search requires a vector profile to compute similarity.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The 'descriptionVector' field is set as retrievable: false
Why it's wrong here
Retrievable setting does not affect search relevance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse field attributes (searchable, retrievable) with the vector-specific configuration required for similarity scoring, leading them to pick options A or D instead of recognizing the missing vector configuration.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Search uses a vector configuration object that defines the algorithm (e.g., HNSW) and similarity metric (e.g., cosine) to index and compare vectors. The similarity metric determines how distances between query and document vectors are calculated, which directly influences the relevance score used for sorting. In a real-world scenario, if you omit the similarity metric, the index defaults to no ranking, and results may appear in insertion order or arbitrary order, which is a common pitfall when migrating from keyword-based search to vector search.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: The index does not have a vector configuration with a similarity metric — Option C is correct because Azure AI Search requires a vector configuration with a similarity metric (e.g., cosine, dotProduct, euclidean) to compute relevance scores for vector search results. Without this configuration, the search engine cannot sort results by relevance, leading to unsorted or default ordering. The similarity metric is defined in the index's vector configuration and is essential for ranking vector query results.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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