Quick Answer
The answer is to increase the number of labeled documents, especially those containing the target entities. This works because recall measures the model’s ability to find all relevant instances of an entity, and low recall typically means the model is missing patterns that appear in your data. By adding more labeled examples that feature the target entities, you provide the custom NER model with richer, more varied contexts to learn from, directly addressing the root cause of missed predictions. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of how data quality and quantity affect model performance, often appearing as a trap where candidates mistakenly adjust confidence thresholds or training hours. Remember, precision is about being correct when you guess, but recall is about not missing any true positives. A helpful memory tip: recall is like a net—widen it with more labeled examples to catch every fish.
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 building a custom named entity recognition (NER) model using Azure AI Language. After labeling 200 documents, you train the model and achieve 85% precision but only 60% 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.
Lower the confidence threshold
Why wrong: Lowering threshold may improve recall but reduces precision.
Increase the training hours
Why wrong: Training hours are not adjustable by users.
Increase the number of labeled documents, especially those containing the target entities
More examples improve recall.
Switch to a different Azure AI Language feature
Why wrong: Stick with custom NER; other features are not designed for this.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the number of labeled documents, especially those containing the target entities
Recall is the ability to find all relevant instances. Adding more labeled examples with the target entities will help the model recognize more patterns. Increasing training hours doesn't guarantee improvement; adjusting confidence threshold lowers precision; using a different service is not necessary.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Lower the confidence threshold
Why it's wrong here
Lowering threshold may improve recall but reduces precision.
Increase the training hours
Why it's wrong here
Training hours are not adjustable by users.
Increase the number of labeled documents, especially those containing the target entities
Why this is correct
More examples improve recall.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Switch to a different Azure AI Language feature
Why it's wrong here
Stick with custom NER; other features are not designed for this.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
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FAQ
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Static NAT maps one inside address to one outside address..
The correct answer is: Increase the number of labeled documents, especially those containing the target entities — Recall is the ability to find all relevant instances. Adding more labeled examples with the target entities will help the model recognize more patterns. Increasing training hours doesn't guarantee improvement; adjusting confidence threshold lowers precision; using a different service is not necessary.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Static NAT maps one inside address to one outside address.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are building a chatbot that uses Azure AI Language to extract intents and entities from user utterances. The bot must recognize custom entities like product names that are not in the default model. Which feature should you use?
easyWhy C: Option C is correct because a custom named entity recognition (NER) component allows you to train a model to extract custom entities. Option A is wrong because the prebuilt entity recognition only recognizes common entities. Option B is wrong because a list entity in a conversational language understanding (CLU) project is for matching exact terms, not for training a model. Option D is wrong because key phrase extraction does not extract entities.
Variation 2. You are building a custom entity extraction solution using Azure AI Language. You have a small dataset (50 documents) with annotated entities. You need to train a model that can extract similar entities from new documents. What is the best approach?
mediumWhy A: Option C is correct because Custom NER allows you to train a model with a small dataset, especially if you use active learning to improve. Option A is wrong because the prebuilt entity recognition does not cover custom entities. Option B is wrong because the built-in extractor skills in AI Search are for indexing, not for custom entity extraction. Option D is wrong because the Conversational PII entity extraction is for PII only.
Last reviewed: Jun 20, 2026
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