AI-102 Practice Question: Implement natural language processing solutions
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.
A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model 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: "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 training data for the 'billing' category and correct any mislabeled examples.
Option C is correct because false positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.
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.
✗
Add more training data to all categories to improve overall model performance.
Why it's wrong here
Adding more data without addressing mislabeling may not reduce false positives for 'billing'.
✗
Use a different Azure AI service, such as key phrase extraction, to identify billing-related content.
Why it's wrong here
Key phrase extraction is not designed for classification.
✓
Review the training data for the 'billing' category and correct any mislabeled examples.
Why this is correct
Correcting mislabeled examples improves the model's ability to distinguish categories.
Clue confirmation
The clue words "best", "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the confidence threshold for the 'billing' category to reduce false positives.
Why it's wrong here
This is a post-processing step that does not fix the model; it may also miss true positives.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often jump to a threshold adjustment (Option D) as a quick fix, but Azure's custom text classification models require data quality improvements first, as confidence thresholds only affect prediction output, not model accuracy.
Trap categories for this question
Keyword trap
Key phrase extraction is not designed for classification.
Detailed technical explanation
How to think about this question
Custom text classification in Azure Cognitive Service for Language uses a transformer-based model fine-tuned on your labeled data. False positives for a category often indicate that the training examples for that category contain features (e.g., words, phrases) that overlap with other categories, or that some examples are incorrectly labeled. Correcting mislabeled examples directly improves the decision boundary learned by the model, which is more effective than post-hoc threshold tuning or adding unrelated data.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Review the training data for the 'billing' category and correct any mislabeled examples. — Option C is correct because false positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.
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: "best", "first". 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?
Read the scenario before looking for a memorised answer.
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Question Discussion
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