The answer is to add more labeled examples covering the missed entities. Low recall in a custom NER model means the model is failing to identify many true positive entities, often because those entity patterns are underrepresented in the training data. By supplementing your 500 labeled documents with additional examples that specifically include the missed entities, you directly address the root cause—the model lacks sufficient exposure to those variations to generalize correctly. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of the precision-recall tradeoff and the practical steps to improve model performance without overfitting. A common trap is to increase training epochs, but that risks memorizing noise rather than learning new patterns. Remember the memory tip: “Recall requires representation”—if the model can’t recall it, you haven’t shown it enough examples.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are deploying a custom Named Entity Recognition (NER) model using Azure AI Language. You have 500 labeled documents. After training, the model shows high precision but low recall. Which action is most likely to improve recall?
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
✓
Add more labeled examples covering the missed entities
Low recall means many true entities are missed. Adding more labeled examples that include the missed entities helps the model learn to recognize them. Increasing training epochs may lead to overfitting. Changing the model to a different type (e.g., CLU) is not appropriate for NER. Reducing the confidence threshold would allow more predictions but could lower precision, and the question targets recall specifically.
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.
✗
Switch to Conversational Language Understanding
Why it's wrong here
CLU is for intent/entity extraction from conversational data, not custom NER.
✗
Reduce the confidence threshold for entity extraction
Why it's wrong here
Lowering threshold increases predictions but may also increase false positives; does not specifically improve recall.
✓
Add more labeled examples covering the missed entities
Why this is correct
More diverse examples help the model generalize and catch more true entities.
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.
✗
Increase the number of training epochs
Why it's wrong here
More epochs may overfit and not necessarily improve recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add more labeled examples covering the missed entities — Low recall means many true entities are missed. Adding more labeled examples that include the missed entities helps the model learn to recognize them. Increasing training epochs may lead to overfitting. Changing the model to a different type (e.g., CLU) is not appropriate for NER. Reducing the confidence threshold would allow more predictions but could lower precision, and the question targets recall specifically.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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