AI-103 Text Analysis Practice Question
You are using Azure AI Language custom text classification. You have uploaded your training dataset and trained a model. You notice that your evaluation metrics show low precision. What does low precision indicate about your model?
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
✓
Many of the items predicted as a specific class actually belonged to a different class.
Precision measures the ratio of true positive predictions to total predicted positives. Low precision means the model produces many false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Many of the items predicted as a specific class actually belonged to a different class.
Why this is correct
Low precision indicates high false positives, meaning items were incorrectly labeled as the target class.
- ✗
The model failed to find many actual positive instances in the dataset.
Why it's wrong here
Failing to find actual instances describes low recall.
- ✗
The latency of the REST API calls is too high.
Why it's wrong here
API latency is an infrastructure metric, not a model evaluation metric.
- ✗
The training dataset contained fewer than 5 documents per class.
Why it's wrong here
Dataset size guidelines affect overall performance, but precision specifically reflects false positive rates.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-103 exam.