MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 deep learning models, each 500 MB. The total model size is 5 GB, which exceeds the 5 GB limit for SageMaker real-time endpoints. The data scientist wants to reduce the model size without significantly impacting accuracy. The ensemble uses averaging of predictions from all models. The scientist has access to a validation set with 10,000 samples. Which technique should the scientist use to reduce the model size?
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
✓
Use model distillation to train a smaller model that approximates the ensemble
Model distillation trains a smaller student model to mimic the ensemble, reducing size while preserving accuracy. Option B is wrong because price-aware instance selection does not reduce model size. Option C is wrong because SageMaker Neo is for optimization, not size reduction below 5 GB. Option D is wrong because pruning alone may not reduce size enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use model distillation to train a smaller model that approximates the ensemble
Why this is correct
Distillation produces a compact model with similar performance.
- ✗
Use a more expensive instance type to host the model
Why it's wrong here
Does not reduce model size.
- ✗
Use SageMaker Neo to compile and optimize the model
Why it's wrong here
Neo optimizes for inference speed, not necessarily reducing size below 5 GB.
- ✗
Apply weight pruning to each model in the ensemble
Why it's wrong here
Pruning may reduce size but not enough to meet 5 GB limit.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.