- A
Use a serverless endpoint configuration to automatically scale.
Why wrong: Serverless endpoints do not support GPU instances.
- B
Use a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic.
Multi-model endpoints allow efficient resource utilization and cost savings.
- C
Use Spot instances for the endpoint to reduce cost.
Why wrong: Spot instances can be terminated at any time, making them unsuitable for low-latency real-time inference.
- D
Provision multiple on-demand GPU instances behind a load balancer.
Why wrong: This is costly and may over-provision for normal traffic.
- E
Use Amazon SageMaker Elastic Inference to attach GPU acceleration to a CPU instance.
Elastic Inference provides GPU acceleration at a lower cost than full GPU instances.
Quick Answer
The correct answer is to use a multi-model endpoint with a mix of CPU and GPU instances. This approach directly addresses cost optimization for SageMaker GPU inference with unpredictable traffic by allowing the endpoint to dynamically route requests: GPU instances handle the large deep learning model’s heavy inference during bursts, while CPU instances serve lighter loads or fallback traffic, avoiding the expense of over-provisioning dedicated GPU capacity. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of SageMaker inference design patterns for variable workloads—a common trap is assuming Elastic Inference is still viable (it is deprecated) or that a single GPU instance type can cost-effectively handle sporadic spikes. Instead, remember that mixing instance types on a single endpoint lets you pay only for the GPU acceleration you actually use during bursts. Memory tip: “Mix for the spikes, save on the dips.”
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
SageMaker multi-model endpoints use a shared container model where the inference container loads and unloads model artifacts from Amazon EFS or S3 on demand, allowing multiple models to share the same instance. This architecture reduces cost by improving instance utilization, but it introduces a cold-start latency when a model is first loaded; however, for unpredictable bursts, the trade-off is acceptable if the model is frequently accessed. Elastic Inference (Option E) attaches a small, dedicated GPU acceleration to a CPU instance, providing GPU compute for inference without the full cost of a GPU instance, but it has limited memory and may not support very large deep learning models that require significant GPU memory.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic. — Option B is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data science team deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?
easy- A.ml.m5.2xlarge
- B.ml.p4d.24xlarge
- ✓ C.ml.p3.2xlarge
- D.ml.c5.2xlarge
Why C: Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.
Last reviewed: Jun 24, 2026
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.
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