Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your company uses Azure Cognitive Search to index millions of documents. Users report that search results include irrelevant documents. You need to improve search relevance by boosting documents that contain the search term in the title field. Which scoring profile configuration should you use?
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
✓
Create a tagging scoring profile that boosts by the title field with a weight of 10.
A tagging scoring profile boosts documents based on matching tags from a specific field (like title), and the weight parameter controls the boost magnitude. Option B is incorrect because a freshness scoring profile boosts by recency, not by term presence in a field. Option C is incorrect because a distance scoring profile boosts based on geospatial proximity, not text fields. Option D is incorrect because a magnitude scoring profile boosts based on numeric field values, not text fields; a linear boosting function is used for magnitude profiles, but this does not apply to boosting by title.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a tagging scoring profile that boosts by the title field with a weight of 10.
Why this is correct
A tagging profile boosts documents that have matching terms in a specific field, like title.
- ✗
Create a freshness scoring profile with a boosting duration of 30 days.
Why it's wrong here
Freshness scoring boosts based on how recent a document is.
- ✗
Create a distance scoring profile with a reference point parameter.
Why it's wrong here
Distance scoring is for geospatial location boosting.
- ✗
Create a magnitude scoring profile with a boosting function of 'linear'.
Why it's wrong here
Magnitude scoring boosts based on numeric values, not text fields.
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