AI-102 Practice Question: Implement natural language processing solutions
Which THREE factors should be considered when choosing between Azure AI Language's pre-built sentiment analysis and custom sentiment analysis for a specialized domain?
⚠ Common exam trap
It's easy for candidates to assume custom models are always superior or faster, overlooking the critical requirement for labeled training data and the fact that pre-built models already offer robust multilingual support and container deployment options.
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
✓
Custom models require a large set of labeled training data.
Custom sentiment analysis in Azure AI Language requires a sufficiently large set of labeled training data to fine-tune a model for a specialized domain. Without this data, the custom model cannot learn domain-specific sentiment patterns, making it impractical for scenarios where labeled data is scarce.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Custom models require a large set of labeled training data.
Why this is correct
Custom models need labeled data for training.
- ✗
Custom models always have faster response times.
Why it's wrong here
Response times are generally comparable.
- ✓
The pre-built model may not accurately handle domain-specific jargon.
Why this is correct
Pre-built models are trained on general data, so may miss domain-specific terms.
- ✗
Pre-built models cannot be used in containers.
Why it's wrong here
Pre-built models can be deployed in containers.
- ✓
Pre-built models offer multilingual support out-of-the-box.
Why this is correct
Pre-built sentiment analysis supports many languages, but accuracy may vary for specialized domains.
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This AI-102 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-102 exam.