hardMultiple Choice
MLA-C01 Practice Question: Use a pre-trained NLP model from SageMaker…
A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?
⚠ Common exam trap
AWS often tests the misconception that pre-trained models require fine-tuning or additional data preparation before inference, when in fact they can be used directly for predictions after deployment to an endpoint.
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
✓
Deploy the model to an endpoint
D is correct because SageMaker JumpStart provides pre-trained models that are ready for inference without additional training. To make predictions, you must deploy the model to a SageMaker endpoint, which creates a hosted inference endpoint that can accept input data and return sentiment analysis results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Label the dataset for fine-tuning
Why it's wrong here
Labelling data for fine-tuning is optional; JumpStart provides pre-trained models that can serve predictions immediately after deployment. The required step is deploying the model to an endpoint. Labelling would be correct when adapting a foundation model to a domain-specific classification task.
- ✗
Train the model from scratch on the company's data
Why it's wrong here
JumpStart models are already trained, so retraining from scratch wastes compute and is not required to obtain predictions; deployment to an endpoint suffices. Training from scratch is tempting when labelled domain data exists and the pre-trained model's accuracy on specialised vocabulary is insufficient, making fine-tuning or custom training the genuine requirement.
- ✗
Convert the model to ONNX format
Why it's wrong here
JumpStart provides deployable pre-trained models that generate predictions directly after endpoint deployment; ONNX conversion is unnecessary and would discard the native inference container. ONNX is tempting when exporting models for cross-runtime portability, such as running inference outside SageMaker on edge devices or alternative serving stacks.
- ✓
Deploy the model to an endpoint
Why this is correct
Deploying the JumpStart model to a SageMaker endpoint provisions a hosted inference container that serves real-time prediction requests. Without this deployment step, the pre-trained NLP model cannot be invoked for sentiment analysis, so it is required to obtain predictions.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.