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AI-102 Recall Practice Question

Exhibit

Refer to the exhibit. You have the following JSON policy from an Azure AI Language custom entity extraction project evaluation:

{
  "evaluation": {
    "entities": {
      "ProductName": {
        "precision": 0.92,
        "recall": 0.65,
        "f1": 0.76
      },
      "OrderNumber": {
        "precision": 0.88,
        "recall": 0.90,
        "f1": 0.89
      },
      "Date": {
        "precision": 0.95,
        "recall": 0.85,
        "f1": 0.90
      }
    }
  }
}

Based on the exhibit, which entity should you focus on improving by adding more labeled examples?

⚠ Common exam trap

The trap is that candidates may choose 'All entities need improvement' (Option C) because they overlook the recall scores shown in the exhibit. While ProductName has low recall (0.65), Date and OrderNumber have very high recall (0.98 and 0.99), indicating they are already performing well. The pitfall is failing to compare the scores and identify the one entity with significantly lower recall.

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

✓

ProductName

The exhibit shows that ProductName has a recall of 0.65, which is lower than the recall for Date (0.98) and OrderNumber (0.99). Low recall indicates that the model is missing many true instances of ProductName. Adding more labeled examples specifically for ProductName will help the model learn its patterns better, improving recall and overall performance. This aligns with the practice of iterative model improvement in custom entity extraction within Azure AI Language.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Date
Why it's wrong here
If the exhibit shows the Date entity already meeting its accuracy or confidence threshold, adding labelled examples there wastes effort. The entity with the lowest scores needs more data. Date is tempting because dates appear frequently in documents, but frequency alone does not indicate poor model performance.
✗
OrderNumber
Why it's wrong here
If the exhibit shows OrderNumber already achieving acceptable precision, recall or confidence, extra labelled examples there will not lift overall model quality. The weakest entity needs the data. OrderNumber is tempting because it is a structured, high-value field, but strong existing scores rule it out.
✗
All entities need improvement.
Why it's wrong here
Adding labelled examples to every entity dilutes effort when the exhibit shows some entities already meeting their thresholds. Only the lowest-scoring entity needs more data. This option is tempting as a safe catch-all, but it ignores the exhibit's per-entity metrics that identify the actual weak point.
✓
ProductName
Why this is correct
ProductName shows the weakest per-entity precision and recall in the exhibit, so adding labelled examples for it yields the greatest accuracy gain. Improving entities already scoring highly would waste labelling effort without lifting overall model performance.

Go deeper

AI-102 Recall Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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