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Machine Learning Implementation and Operations →hardMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 real-time inference endpoint using SageMaker. The model has a high memory footprint and requires GPU acceleration. Which instance type and configuration should be used to minimize cost while meeting latency requirements?

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.

A
ml.p3.2xlarge with 1 GPU
Good balance of GPU and memory for high-memory models at reasonable cost.
B
ml.g4dn.xlarge with 1 GPU
Why wrong: Less memory than p3.2xlarge, may cause OOM errors.
C
ml.c5.xlarge with no GPU
Why wrong: No GPU, so cannot meet GPU acceleration requirement.
D
ml.p3.16xlarge with 8 GPUs
Why wrong: Overprovisioned and costly for a single model.

Full breakdown with real-world context →

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

✓

ml.p3.2xlarge with 1 GPU

Option A (ml.p3.2xlarge with 1 GPU) is correct because it provides the required GPU acceleration for the high-memory-footprint model while using the smallest instance in the P3 family, which minimizes cost. The P3 instances use NVIDIA V100 GPUs with high memory bandwidth, suitable for real-time inference with low latency, and the 2xlarge size offers sufficient GPU memory 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.

✓
ml.p3.2xlarge with 1 GPU
Why this is correct
Good balance of GPU and memory for high-memory models at reasonable 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.
✗
ml.g4dn.xlarge with 1 GPU
Why it's wrong here
Less memory than p3.2xlarge, may cause OOM errors.
✗
ml.c5.xlarge with no GPU
Why it's wrong here
No GPU, so cannot meet GPU acceleration requirement.
✗
ml.p3.16xlarge with 8 GPUs
Why it's wrong here
Overprovisioned and costly for a single model.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume a larger instance with more GPUs (like ml.p3.16xlarge) is needed for high-memory models, but the question specifically asks to minimize cost while meeting latency, so the smallest GPU instance that fits the model is optimal.

Detailed technical explanation

How to think about this question

Under the hood, SageMaker real-time endpoints use a single-instance deployment by default, and the model's memory footprint must fit within the GPU memory of the chosen instance. The P3 family provides up to 16 GB of GPU memory on the 2xlarge, which is often sufficient for large models like BERT-large or ResNet-152, while the G4dn instances offer only 16 GB of GPU memory on the xlarge but with slower memory bandwidth (300 GB/s vs 900 GB/s on V100). In practice, choosing a smaller GPU instance can still meet latency Service Level Agreements (SLAs) if the model is optimized with techniques like TensorRT or ONNX Runtime.

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.

Visual reference

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 MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: ml.p3.2xlarge with 1 GPU — Option A (ml.p3.2xlarge with 1 GPU) is correct because it provides the required GPU acceleration for the high-memory-footprint model while using the smallest instance in the P3 family, which minimizes cost. The P3 instances use NVIDIA V100 GPUs with high memory bandwidth, suitable for real-time inference with low latency, and the 2xlarge size offers sufficient GPU memory without over-provisioning.

What should I do if I get this MLS-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.

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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.