The answer is to review the test set results and add more labeled examples for entities with low precision or recall. This is the correct first step because model accuracy in Azure AI Language’s custom NER is fundamentally driven by the quality and coverage of your labeled data; reviewing test set errors reveals specific patterns where the model is failing, and augmenting those weak spots directly addresses the root cause of poor performance. On the AI-102 exam, this scenario tests your understanding of the iterative data refinement cycle—a common trap is to immediately adjust hyperparameters like epochs or reduce entity types, but Microsoft emphasizes that data improvement comes before model tuning. Remember the memory tip: “Fix the data, not the dials”—always diagnose test set errors before tweaking training settings.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 have a custom Named Entity Recognition (NER) model trained using Azure AI Language. The model is performing poorly on new data. You need to improve its accuracy. Which action should you take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Review the test set results and add more labeled examples for entities with low precision/recall.
Option B is correct because the first step to improve model accuracy is to review the test set results and identify errors, then add more labeled examples for those failing patterns. Option A is wrong because reducing the number of entities might lose important distinctions. Option C is wrong because retraining with the same data will not fix issues. Option D is wrong because increasing epochs may lead to overfitting without better data.
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
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the training epochs.
Why it's wrong here
May overfit without better data; not the first step.
✗
Retrain the model using the same training data.
Why it's wrong here
Will not improve performance as the data hasn't changed.
✓
Review the test set results and add more labeled examples for entities with low precision/recall.
Why this is correct
Adding targeted training data helps the model learn patterns it missed.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Reduce the number of entity types in the model.
Why it's wrong here
May not address the root cause and could lose needed entity types.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
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
How to think about this question
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.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
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
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. 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
Got this wrong? Here's your next step.
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.
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Review the test set results and add more labeled examples for entities with low precision/recall. — Option B is correct because the first step to improve model accuracy is to review the test set results and identify errors, then add more labeled examples for those failing patterns. Option A is wrong because reducing the number of entities might lose important distinctions. Option C is wrong because retraining with the same data will not fix issues. Option D is wrong because increasing epochs may lead to overfitting without better data.
What should I do if I get this AI-102 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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