AI-103 Text Analysis Practice Question
When configuring a custom Named Entity Recognition (NER) project in Language Studio using the Azure AI Language service, which THREE steps or configuration options are required during the model lifecycle?
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
✓
Define your entity schema and types
Building a custom NER model involves defining the entity schema, tagging or labeling your documents, training the model, evaluating performance metrics, and finally deploying the trained model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define your entity schema and types
Why this is correct
You must define the entity types (e.g., ProductNumber, VendorName) before labeling data.
- ✗
Fine-tune the underlying base transformer weights via direct GPU gradient backpropagation code
Why it's wrong here
Azure AI Language abstracts model training in Language Studio or via APIs without requiring manual GPU gradient coding.
- ✗
Manually convert all input text files into pre-tokenized JSON vectors
Why it's wrong here
Language Studio handles tokenization and vectorization automatically during training.
- ✓
Train the model using your labeled dataset
Why this is correct
Training is a required lifecycle step to build the machine learning model based on your tagged data.
- ✓
Deploy the trained model to a deployment name for client application consumption
Why this is correct
A trained model must be explicitly deployed to a deployment slot before you can make prediction calls against it.
About these practice questions
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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.