This MLS-C01 practice question tests your understanding of modeling. 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 for real-time fraud detection. The model must have extremely low latency (<10 ms) and high throughput. Which THREE design choices meet these requirements? (Choose 3.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use GPU instances (e.g., ml.p3) for the endpoint.
Option A is correct because GPU instances like ml.p3 provide massively parallel compute capability that accelerates matrix operations common in deep learning models, enabling inference latencies under 10 ms. For real-time fraud detection, the GPU's high throughput and low latency are essential for processing thousands of transactions per second without bottlenecks.
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 GPU instances (e.g., ml.p3) for the endpoint.
Why this is correct
GPU accelerates inference, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use one endpoint per model to avoid interference.
Why it's wrong here
Multiple endpoints increase cost and may not improve latency.
✗
Use SageMaker Batch Transform for real-time predictions.
Why it's wrong here
Batch transform is for offline, not real-time.
✓
Use SageMaker multi-model endpoints to host multiple models on the same instance.
Why this is correct
Multi-model endpoints improve resource utilization and reduce latency for multiple models.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use SageMaker Elastic Inference to attach GPU acceleration to a CPU instance.
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that batch processing services like Batch Transform can be used for real-time inference, but the key distinction is that Batch Transform is designed for offline, asynchronous workloads and cannot meet low-latency requirements.
Detailed technical explanation
How to think about this question
SageMaker multi-model endpoints (Option D) dynamically load and unload models into memory on a shared instance, using a container that caches model artifacts from Amazon S3, which reduces cold-start latency compared to separate endpoints. Elastic Inference (Option E) attaches a fraction of GPU acceleration to a CPU instance, providing cost-effective acceleration for models that don't require a full GPU, but it introduces additional network overhead for tensor operations that can add 1-3 ms latency, which must be accounted for in strict sub-10 ms SLAs.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use GPU instances (e.g., ml.p3) for the endpoint. — Option A is correct because GPU instances like ml.p3 provide massively parallel compute capability that accelerates matrix operations common in deep learning models, enabling inference latencies under 10 ms. For real-time fraud detection, the GPU's high throughput and low latency are essential for processing thousands of transactions per second without bottlenecks.
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.
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.
Question Discussion
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