MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to deploy a machine learning model for real-time inference. The model was trained using XGBoost and achieves high accuracy. However, during deployment, the endpoint returns a 'ModelError' when receiving input data. The input is a CSV string. What is the most likely cause?
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
✓
The input data format does not match the model's expected format (e.g., CSV vs JSON)
The most common cause of ModelError during inference is that the input format does not match what the model expects. XGBoost models typically expect CSV without headers. The serializer setting in SageMaker must be configured correctly. If the model expects text/csv but the endpoint is configured as JSON, the error occurs. The other options are less likely: model weights are loaded correctly if the model deployed, and the instance type affects latency not errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The input data format does not match the model's expected format (e.g., CSV vs JSON)
Why this is correct
SageMaker inference endpoints require the input to be in the format expected by the model, e.g., CSV for XGBoost.
- ✗
The inference instance type is too small
Why it's wrong here
Instance size affects performance, not input parsing.
- ✗
The model is not properly loaded into memory
Why it's wrong here
If the model were not loaded, the endpoint would not be healthy.
- ✗
The model weights are corrupted during deployment
Why it's wrong here
Model corruption would cause a different error, typically during loading.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.