MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company is deploying a large language model (LLM) to a SageMaker endpoint. They want to minimize inference latency and cost by using GPU acceleration and model parallelism. The model is too large to fit on a single GPU. Which SageMaker feature should they use?
⚠ Common exam trap
Many exam-takers confuse the distributed data parallel library with model parallelism; data parallel is for training and does not split the model.
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
✓
SageMaker model parallelism library
The SageMaker model parallelism library is designed to partition large models across multiple GPUs, enabling inference for models that exceed a single GPU's memory. It supports various parallelism strategies and can reduce latency and cost. The other options are either for training, deprecated, or only provide recommendations, not the required model parallelism.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Elastic Inference is a deprecated feature that attaches low-cost GPU acceleration to CPU instances. It is not designed for large models that require model parallelism and does not support splitting a model across multiple GPUs. It is also no longer recommended for new deployments.
- ✗
SageMaker distributed data parallel library
Why it's wrong here
The distributed data parallel library is used for training, not inference. It replicates the model across multiple GPUs and splits the data, which does not help when the model is too large to fit on a single GPU. For inference of large models, model parallelism is required.
- ✓
SageMaker model parallelism library
Why this is correct
The SageMaker model parallelism library enables training and inference of large models that cannot fit on a single GPU by partitioning the model across multiple GPUs. It supports tensor parallelism and pipeline parallelism, and it can be used for inference to reduce latency and cost. This directly addresses the need for model parallelism with GPU acceleration.
- ✗
SageMaker Inference Recommender
Why it's wrong here
Inference Recommender helps select the optimal instance type and configuration for a model, but it does not provide model parallelism. It benchmarks your model on different instance types and provides recommendations. It does not enable splitting a model across multiple GPUs for inference.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.