- A
Amazon Elastic Inference
Elastic Inference attaches a GPU accelerator to a CPU instance, providing GPU acceleration at lower cost.
- B
SageMaker Neo compilation
Why wrong: Neo optimizes model for hardware but does not provide GPU acceleration.
- C
Use a smaller GPU instance like ml.g4dn.xlarge instead of ml.p3.2xlarge
Choosing a smaller GPU instance can reduce cost while still providing GPU acceleration.
- D
Quantize the model to INT8 precision
Why wrong: Quantization reduces model size and speeds up inference but does not add GPU acceleration.
- E
Use SageMaker serverless inference with GPU
Why wrong: Serverless inference does not support attached GPUs; it uses CPU only.
Question 655 of 1,000
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 wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (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
✓
Amazon Elastic Inference
Amazon Elastic Inference (Option A) allows you to attach a smaller, configurable GPU acceleration resource to a SageMaker endpoint, enabling GPU-accelerated inference without the cost of a full GPU instance. This directly meets the requirement of minimizing costs by using a smaller attached GPU.
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.
- ✓
Amazon Elastic Inference
Why this is correct
Elastic Inference attaches a GPU accelerator to a CPU instance, providing GPU acceleration at lower cost.
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.
- ✗
SageMaker Neo compilation
Why it's wrong here
Neo optimizes model for hardware but does not provide GPU acceleration.
- ✓
Use a smaller GPU instance like ml.g4dn.xlarge instead of ml.p3.2xlarge
Why this is correct
Choosing a smaller GPU instance can reduce cost while still providing GPU acceleration.
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.
- ✗
Quantize the model to INT8 precision
Why it's wrong here
Quantization reduces model size and speeds up inference but does not add GPU acceleration.
- ✗
Use SageMaker serverless inference with GPU
Why it's wrong here
Serverless inference does not support attached GPUs; it uses CPU only.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse SageMaker Neo compilation (a model optimization technique) with hardware acceleration, or mistakenly think SageMaker serverless inference supports GPU, when in fact it only supports CPU-based compute.
Detailed technical explanation
How to think about this question
Amazon Elastic Inference works by attaching a separate GPU acceleration resource (e.g., an 'eia1.medium' or 'eia2.xlarge' accelerator) to a SageMaker endpoint running on a CPU instance, allowing the model to offload matrix operations to the accelerator via the TensorFlow or PyTorch Elastic Inference API. This is distinct from using a full GPU instance like ml.g4dn.xlarge, which includes a built-in GPU (e.g., T4) and is itself a smaller GPU instance option (Option C). In practice, Elastic Inference is ideal for models with moderate GPU demand, such as BERT-base, where a full GPU instance would be underutilized.
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.
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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: Amazon Elastic Inference — Amazon Elastic Inference (Option A) allows you to attach a smaller, configurable GPU acceleration resource to a SageMaker endpoint, enabling GPU-accelerated inference without the cost of a full GPU instance. This directly meets the requirement of minimizing costs by using a smaller attached GPU.
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
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Last reviewed: Jul 4, 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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