- A
Use Amazon Elastic Inference to attach an EI accelerator to the endpoint instance
Why wrong: EI reduces inference time per request but does not increase the number of requests the endpoint can handle concurrently.
- B
Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge
Auto scaling adds instances to handle load; a larger instance reduces per-request latency.
- C
Switch to a multi-model endpoint to serve multiple models on the same instance
Why wrong: Multi-model endpoints help with model loading but do not directly address capacity for a single model.
- D
Replace the SageMaker endpoint with an AWS Lambda function that loads the model from S3 and returns predictions
Why wrong: Lambda has timeout limits and may not be suitable for complex model inference with 200 ms latency.
Quick Answer
The answer is to configure the SageMaker endpoint with Application Auto Scaling based on the InvocationsPerInstance metric and upgrade to a larger instance type like ml.c5.xlarge. This is correct because a single ml.c5.large instance can only handle roughly 5 requests per second given a 200 ms inference time, so a spike to 100 requests per second overwhelms the instance, causing high latency and 504 errors from request queue overflow. Auto Scaling on InvocationsPerInstance dynamically adds instances to match concurrency demand, while the larger instance doubles compute capacity, directly reducing per-request latency. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of SageMaker endpoint scaling mechanics versus simply increasing instance size—a common trap is choosing only a larger instance without addressing concurrency limits. Remember the memory tip: “One instance, five requests; scale out for the rest.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 runs an e-commerce platform on AWS. They have a SageMaker endpoint serving a product recommendation model. The model uses a custom container with a TensorFlow model. Recently, the endpoint has been returning high latency and occasional 504 errors during peak traffic. The data scientist observes that the model inference time is around 200 ms per request, but the endpoint is configured with a single ml.c5.large instance. The traffic spikes can reach 100 requests per second. The data scientist needs to reduce latency and eliminate 504 errors. Which course of action is most appropriate?
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
Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge
Option B is correct because the endpoint is bottlenecked by both instance size and concurrency. With a single ml.c5.large instance handling 100 requests per second and a 200 ms inference time, the instance can only process about 5 requests per second (1000 ms / 200 ms = 5 requests per second per instance). Application Auto Scaling based on the 'InvocationsPerInstance' metric will add instances during traffic spikes, while upgrading to ml.c5.xlarge doubles compute capacity per instance, reducing latency and eliminating 504 errors caused by request queue overflow.
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 Amazon Elastic Inference to attach an EI accelerator to the endpoint instance
Why it's wrong here
EI reduces inference time per request but does not increase the number of requests the endpoint can handle concurrently.
- ✓
Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge
Why this is correct
Auto scaling adds instances to handle load; a larger instance reduces per-request latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a multi-model endpoint to serve multiple models on the same instance
Why it's wrong here
Multi-model endpoints help with model loading but do not directly address capacity for a single model.
- ✗
Replace the SageMaker endpoint with an AWS Lambda function that loads the model from S3 and returns predictions
Why it's wrong here
Lambda has timeout limits and may not be suitable for complex model inference with 200 ms latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse performance bottlenecks with model optimization or cost-saving strategies, and incorrectly choose Elastic Inference or multi-model endpoints, which address different problems (GPU acceleration or multi-model hosting) rather than the core issue of insufficient compute capacity and lack of auto scaling.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints use an internal load balancer that distributes requests across instances; when a single ml.c5.large instance is overwhelmed, requests queue up and eventually time out with 504 errors. The 'InvocationsPerInstance' metric is a custom CloudWatch metric that tracks the number of invocations per instance, and Application Auto Scaling can be configured with a target value (e.g., 5 invocations per instance) to trigger scale-out events. In practice, the 200 ms inference time means each instance can handle at most ~5 requests per second, so for 100 requests per second, at least 20 instances are needed; upgrading to ml.c5.xlarge doubles the vCPUs and memory, improving per-instance throughput and reducing the number of instances required.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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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Modeling — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Configure the SageMaker endpoint with Application Auto Scaling to scale out based on the 'InvocationsPerInstance' metric, and use a larger instance type such as ml.c5.xlarge — Option B is correct because the endpoint is bottlenecked by both instance size and concurrency. With a single ml.c5.large instance handling 100 requests per second and a 200 ms inference time, the instance can only process about 5 requests per second (1000 ms / 200 ms = 5 requests per second per instance). Application Auto Scaling based on the 'InvocationsPerInstance' metric will add instances during traffic spikes, while upgrading to ml.c5.xlarge doubles compute capacity per instance, reducing latency and eliminating 504 errors caused by request queue overflow.
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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Last reviewed: Jun 24, 2026
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.
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