Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Exhibit

{
  "searchResults": {
    "value": [
      {
        "@search.score": 2.345,
        "content": "The quick brown fox jumps over the lazy dog.",
        "metadata_storage_path": "https://storage.blob.core.windows.net/documents/doc1.pdf"
      },
      {
        "@search.score": 1.234,
        "content": "A fast brown fox leaps over a sleepy dog.",
        "metadata_storage_path": "https://storage.blob.core.windows.net/documents/doc2.pdf"
      }
    ]
  }
}

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

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 first document has a higher term frequency and better term proximity for the query terms

The default scoring algorithm in Azure AI Search uses term frequency (TF) and term proximity. The first document likely has a higher term frequency for 'brown' and 'fox', and the terms are closer together (e.g., appearing as a phrase), resulting in a higher score. Option D correctly identifies this. Option A is incorrect because no scoring profile is mentioned. Option B is incorrect because vector search is not used. Option C is incorrect because no semantic ranking is configured.

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 first document has a higher value in a scoring profile field
Why it's wrong here
No scoring profile is evident.
✗
The first document is more similar to the query in vector space
Why it's wrong here
Vector search is not used here.
✗
The first document was boosted by a semantic ranking function
Why it's wrong here
No semantic ranking is shown.
✓
The first document has a higher term frequency and better term proximity for the query terms
Why this is correct
Default scoring favors higher term frequency and proximity.

Go deeper

AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question