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
Labeling is needed only if fine-tuning; pre-trained models can be used directly.
✗
Train the model from scratch on the company's data
Why it's wrong here
JumpStart provides pre-trained models; fine-tuning is optional but not required for basic predictions.
✗
Convert the model to ONNX format
Why it's wrong here
ONNX conversion is not required; JumpStart models are already in a supported format.
✓
Deploy the model to an endpoint
Why this is correct
Deploying to a SageMaker endpoint allows real-time inference on new data.

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MLA-C01 Practice Question: Use a pre-trained NLP model from SageMaker…

Why each option matters

Option-by-option breakdown

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