hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: Deploying a machine learning model using Amazon…
A company is deploying a machine learning model using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The company expects unpredictable traffic patterns with occasional bursts. They want to minimize cost while ensuring low latency during bursts. Which TWO actions should they take? (Select TWO.)
⚠ Common exam trap
Candidates often confuse serverless endpoints with GPU support, not realizing that SageMaker serverless endpoints are CPU-only, and they may overlook that multi-model endpoints can mix instance types to balance cost and performance for bursty GPU workloads.
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 a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic.
A multi-model endpoint with a mix of CPU and GPU instances allows the company to host multiple models on the same endpoint, reducing cost by sharing underlying instances. By including GPU instances, the endpoint can handle the GPU-intensive deep learning inference for the large model, while the CPU instances can serve lighter loads or fallback traffic, ensuring low latency during unpredictable bursts without over-provisioning.
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 a serverless endpoint configuration to automatically scale.
Why it's wrong here
Serverless endpoints do not support GPU instances.
- ✓
Use a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic.
Why this is correct
Multi-model endpoints allow efficient resource utilization and cost savings.
- ✗
Use Spot instances for the endpoint to reduce cost.
Why it's wrong here
Spot instances can be terminated at any time, making them unsuitable for low-latency real-time inference.
- ✗
Provision multiple on-demand GPU instances behind a load balancer.
Why it's wrong here
This is costly and may over-provision for normal traffic.
- ✓
Use Amazon SageMaker Elastic Inference to attach GPU acceleration to a CPU instance.
Why this is correct
Elastic Inference provides GPU acceleration at a lower cost than full GPU instances.
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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.