- A
Leverage the Text Analytics for Health API to extract entities from the support tickets.
Why wrong: Text Analytics for Health is specialized for medical terminology and cannot extract order numbers.
- B
Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
Custom NER with prebuilt components combines ease of use with flexibility for custom categories.
- C
Use the Prebuilt Entity Extraction skill in Azure AI Search to extract order numbers and categories from the text.
Why wrong: Prebuilt Entity Extraction skill is for AI Search indexing, not for real-time entity extraction in the Language service.
- D
Use the Conversational Language Understanding (CLU) project type to train a model for both intent and entity extraction.
Why wrong: CLU is designed for conversational flows, not just entity extraction, and requires more training data.
Quick Answer
The answer is to create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories. This approach is correct because the prebuilt entity components in custom NER can automatically recognize common patterns like “ORD-12345” without requiring custom regex or rule writing, while the custom training handles domain-specific categories like issue types. On the AI-102 exam, this tests your understanding of how Azure AI Language’s custom NER blends prebuilt and custom extraction to minimize development effort and ensure high accuracy, with a common trap being to confuse the Prebuilt Entity Extraction skill in Azure AI Search (which is for indexing, not entity extraction) or to overcomplicate the solution with Conversational Language Understanding. A useful memory tip: think of custom NER as a “hybrid” tool—prebuilt handles the predictable patterns (order numbers), and custom handles the unique business logic (categories).
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 are building a chat bot that uses Azure AI Language to process customer support tickets. The bot must extract entities like order numbers (e.g., ORD-12345) and issue categories. You need to choose the best approach for entity extraction to minimize development effort and ensure high accuracy.
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
Option B is correct because the prebuilt entity components in the custom NER project can recognize common patterns like order numbers without needing to write custom regex or rules. Option A is wrong because the built-in Prebuilt Entity Extraction skill in Azure AI Search is for indexing, not for the Language service. Option C is wrong because the Conversational Language Understanding (CLU) project type is for intent classification and entity extraction, but it requires a lot of data and training. Option D is wrong because the Text Analytics for Health is for medical entities and would not work for order numbers.
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.
- ✗
Leverage the Text Analytics for Health API to extract entities from the support tickets.
Why it's wrong here
Text Analytics for Health is specialized for medical terminology and cannot extract order numbers.
- ✓
Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
Why this is correct
Custom NER with prebuilt components combines ease of use with flexibility for custom categories.
Clue confirmation
The clue words "best", "minimum / minimize" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use the Prebuilt Entity Extraction skill in Azure AI Search to extract order numbers and categories from the text.
Why it's wrong here
Prebuilt Entity Extraction skill is for AI Search indexing, not for real-time entity extraction in the Language service.
- ✗
Use the Conversational Language Understanding (CLU) project type to train a model for both intent and entity extraction.
Why it's wrong here
CLU is designed for conversational flows, not just entity extraction, and requires more training data.
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.
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Implement natural language processing solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AI-102 question test?
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: Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories. — Option B is correct because the prebuilt entity components in the custom NER project can recognize common patterns like order numbers without needing to write custom regex or rules. Option A is wrong because the built-in Prebuilt Entity Extraction skill in Azure AI Search is for indexing, not for the Language service. Option C is wrong because the Conversational Language Understanding (CLU) project type is for intent classification and entity extraction, but it requires a lot of data and training. Option D is wrong because the Text Analytics for Health is for medical entities and would not work for order numbers.
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: "best", "minimum / minimize". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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