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AI-102 Plan and manage an Azure AI solution Practice Question

Exhibit

{
  "properties": {
    "description": "Azure AI Search index for product catalog",
    "fields": [
      {"name": "id", "type": "Edm.String", "key": true, "searchable": false, "filterable": true, "retrievable": true},
      {"name": "productName", "type": "Edm.String", "searchable": true, "filterable": true, "retrievable": true},
      {"name": "category", "type": "Edm.String", "searchable": false, "filterable": true, "retrievable": true},
      {"name": "price", "type": "Edm.Double", "searchable": false, "filterable": true, "retrievable": true, "sortable": true},
      {"name": "descriptionVector", "type": "Collection(Edm.Single)", "searchable": true, "retrievable": false, "dimensions": 1536}
    ]
  }
}

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?

⚠ Common exam trap

Many exam-takers 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.

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

✓

The index does not have a vector configuration with a similarity metric

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.

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.
✗
The 'descriptionVector' field is set as retrievable: false
Why it's wrong here
Retrievable setting does not affect search relevance.

Go deeper

AI-102 Plan and manage an Azure AI solution Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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