The correct answer is to switch to a memory-optimized instance type like r5.large. This resolves the SageMaker endpoint memory bottleneck because the model loads a 2GB dictionary at startup, and under traffic spikes, each ml.c5.xlarge instance runs out of RAM even though CPU utilization remains low at 30%. Memory-optimized instances provide a higher memory-to-vCPU ratio, preventing out-of-memory errors and allowing the endpoint to handle concurrent requests without returning HTTP 503 errors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of right-sizing inference instances based on resource profiles—a common trap is to scale horizontally or add Spot instances, but neither fixes a per-instance memory constraint. Remember the mnemonic: “High memory, low CPU? Go R-series, not more T-series.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 operates a real-time fraud detection system using SageMaker. The model is deployed on an ml.c5.xlarge instance behind an Application Load Balancer (ALB). Recently, during a sales event, traffic spiked and the endpoint returned HTTP 503 errors. The team scaled the instance count from 2 to 5, but errors persisted. CloudWatch metrics show low CPU utilization (~30%) and high memory usage (~90%). The model loads a large dictionary file (2GB) into memory at startup. Which action should resolve the issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a memory-optimized instance type like r5.large.
Option C is correct because the high memory usage indicates the instance type does not have enough memory to handle concurrent requests. Using a memory-optimized instance like r5.large provides more memory per vCPU, reducing memory pressure and preventing OOM errors. Option A is incorrect because low CPU utilization suggests the bottleneck is not CPU. Option B is incorrect because increasing instances does not help if each instance is memory-constrained. Option D is incorrect because using Spot instances may cause interruptions and does not fix the memory issue.
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.
✗
Enable auto-scaling with Spot instances.
Why it's wrong here
Spot instances may be interrupted and do not address the memory bottleneck.
✗
Switch to a compute-optimized instance type like c5.2xlarge.
Why it's wrong here
Compute-optimized instances have similar memory per vCPU; memory may still be insufficient.
✗
Increase the number of instances further to 10.
Why it's wrong here
If each instance is memory-bound, more instances won't help; they may still run out of memory.
✓
Use a memory-optimized instance type like r5.large.
Why this is correct
r5.large has 16GB memory vs 4GB for c5.xlarge, allowing the model to handle more requests.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Compute-optimized instances have similar memory per vCPU; memory may still be insufficient.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Use a memory-optimized instance type like r5.large. — Option C is correct because the high memory usage indicates the instance type does not have enough memory to handle concurrent requests. Using a memory-optimized instance like r5.large provides more memory per vCPU, reducing memory pressure and preventing OOM errors. Option A is incorrect because low CPU utilization suggests the bottleneck is not CPU. Option B is incorrect because increasing instances does not help if each instance is memory-constrained. Option D is incorrect because using Spot instances may cause interruptions and does not fix the memory issue.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
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 company uses SageMaker to host a real-time inference endpoint. The endpoint is receiving a large number of requests, but the latency is higher than expected. The data scientist observes that the CPU utilization is low but memory utilization is high. Which action should be taken to reduce latency?
easy
✓ A.Switch to an instance type with more memory or optimize the model to reduce memory footprint.
B.Enable VPC traffic mirroring to diagnose network issues.
C.Use an instance type with more vCPUs.
D.Increase the number of instances in the endpoint.
Why A: Option A is correct because high memory utilization suggests the model is memory-bound; increasing instance memory or using a model with lower memory footprint can reduce latency. Option B is wrong because CPU utilization is low, so more CPU cores won't help. Option C is wrong because increasing instance count can help throughput but not necessarily latency per request; also it may increase cost. Option D is wrong because the issue is memory, not network.
Last reviewed: Jun 20, 2026
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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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