AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Exhibit
{
"displayName": "CustomQuestionAnsweringProject",
"language": "en",
"description": "QnA for HR policies",
"qnaDocuments": [
{
"id": "doc1",
"source": "HR_Handbook.pdf",
"questions": [
{
"question": "What is the vacation policy?",
"answer": "Employees accrue 15 days per year."
}
]
}
]
}
Refer to the exhibit. You have a Custom Question Answering project configured with the JSON shown. When you test the project in Azure AI Language Studio, the query 'How many vacation days do I get?' returns no answer. What is the most likely cause?
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.
Exhibit
{
"displayName": "CustomQuestionAnsweringProject",
"language": "en",
"description": "QnA for HR policies",
"qnaDocuments": [
{
"id": "doc1",
"source": "HR_Handbook.pdf",
"questions": [
{
"question": "What is the vacation policy?",
"answer": "Employees accrue 15 days per year."
}
]
}
]
}
A
The query is not phrased as an exact match to the trained question.
Custom Question Answering matches questions based on semantic similarity, but if the phrasing is too different, it may not return an answer.
B
The language is set to English but the query uses informal language.
Why wrong: The language setting is appropriate; informal language should not cause a total failure.
C
The answer field is empty in the JSON.
Why wrong: The answer field contains 'Employees accrue 15 days per year.'
D
The confidence score threshold is set too high.
Why wrong: The exhibit does not show any threshold setting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The query is not phrased as an exact match to the trained question.
Custom Question Answering can be configured to require exact matching between the user query and the trained questions. In the exhibit, the JSON likely defines a QnA pair with a specific question, but the test query 'How many vacation days do I get?' does not exactly match the trained question (e.g., it might be slightly different wording). Since the project is set to exact match, the service returns no answer because the query is not an exact match. Option A correctly identifies this cause.
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.
✓
The query is not phrased as an exact match to the trained question.
Why this is correct
Custom Question Answering matches questions based on semantic similarity, but if the phrasing is too different, it may not return an answer.
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.
✗
The language is set to English but the query uses informal language.
Why it's wrong here
The language setting is appropriate; informal language should not cause a total failure.
✗
The answer field is empty in the JSON.
Why it's wrong here
The answer field contains 'Employees accrue 15 days per year.'
✗
The confidence score threshold is set too high.
Why it's wrong here
The exhibit does not show any threshold setting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that 'no answer' results in Azure AI Language Studio are due to high confidence thresholds or empty answer fields. However, in this scenario, the issue is that the query does not exactly match any trained question in the Custom Question Answering project, so the service does not return an answer.
Trap categories for this question
Command / output trap
The exhibit does not show any threshold setting.
Detailed technical explanation
How to think about this question
Under the hood, Custom Question Answering uses a transformer-based model to compute cosine similarity between the query and each question in the project. When the query is an exact string match to a trained question, the similarity score is essentially 1.0, but the service still requires a non-empty answer field to return a response. If the answer field is empty, the service treats the QnA pair as invalid and returns no answer, even if the confidence score is high. This behavior is by design to prevent returning empty responses to users.
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 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: The query is not phrased as an exact match to the trained question. — Custom Question Answering can be configured to require exact matching between the user query and the trained questions. In the exhibit, the JSON likely defines a QnA pair with a specific question, but the test query 'How many vacation days do I get?' does not exactly match the trained question (e.g., it might be slightly different wording). Since the project is set to exact match, the service returns no answer because the query is not an exact match. Option A correctly identifies this cause.
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.
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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