The correct answer is to train the entity extraction model with more examples of dates. This is because custom entity extraction in Azure AI Language relies on supervised learning, where the model learns to recognize patterns from labeled training data; if the model successfully extracts “Paris” as a location but misses “Friday” as a date, it indicates the date entity type lacks sufficient or diverse examples in the training set. On the AI-102 exam, this scenario tests your understanding of how to improve custom named entity recognition (NER) by augmenting training data rather than adjusting model architecture or preprocessing—a common trap is to assume the issue lies with the intent schema or the prebuilt entity component, but the problem is purely with the custom entity’s training coverage. To remember this, think of the “Friday failure fix”: if a custom entity fails, feed it more examples, not more rules.
AI-102 Implement agentic AI solutions Practice Question
This AI-102 practice question tests your understanding of implement agentic ai 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.
A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train the entity extraction model with more examples of dates.
Option C is correct because the custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.
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.
✗
Increase the training data for location entities.
Why it's wrong here
This does not address the date issue.
✗
Add a prebuilt entity component for date.
Why it's wrong here
Prebuilt components may not be suitable for custom models.
✓
Train the entity extraction model with more examples of dates.
Why this is correct
More training examples improve entity recognition.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a different intent classification model.
Why it's wrong here
Entity extraction is separate from intent classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the need for more training data (Option C) with the use of prebuilt components (Option B), assuming prebuilt entities can fix custom model gaps, but prebuilt entities are not part of the custom entity extraction pipeline and would require a different project type.
Detailed technical explanation
How to think about this question
Azure AI Language custom entity extraction uses a learned model that relies on labeled spans in training data. The model's failure to extract 'Friday' indicates it has not seen enough varied date formats (e.g., 'on Friday', 'this Friday', 'Friday the 13th') to generalize. In practice, teams often under-sample temporal expressions, leading to poor recall for date entities, while location entities like 'Paris' are more distinct and easier to learn with fewer examples.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement agentic AI solutions — This question tests Implement agentic AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Train the entity extraction model with more examples of dates. — Option C is correct because the custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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