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 uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?
Increase the instance type to ml.c5.2xlarge.
Larger instance type provides more capacity.
Reduce the scaling cooldown period.
Why wrong: Reducing cooldown may not prevent 5xx errors during sudden spikes.
Place an Application Load Balancer in front of the endpoint.
Why wrong: SageMaker endpoints are not fronted by ALB.
Use Amazon API Gateway to throttle requests.
Why wrong: Throttling may drop requests, not improve availability.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the instance type to ml.c5.2xlarge.
The correct answer is A because upgrading the instance type from ml.m5.large to ml.c5.2xlarge provides more CPU and memory resources, which directly addresses the root cause of 5xx errors during traffic spikes — insufficient compute capacity to handle the request load. Automatic scaling based on CPU utilization may not react quickly enough to sudden spikes, leading to request queuing and timeouts that manifest as 5xx errors. A larger instance type increases the baseline throughput, reducing the likelihood of resource exhaustion before scaling can take effect.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the instance type to ml.c5.2xlarge.
Why this is correct
Larger instance type provides more capacity.
Related concept
Read the scenario before looking for a memorised answer.
Reduce the scaling cooldown period.
Why it's wrong here
Reducing cooldown may not prevent 5xx errors during sudden spikes.
Place an Application Load Balancer in front of the endpoint.
Why it's wrong here
SageMaker endpoints are not fronted by ALB.
Use Amazon API Gateway to throttle requests.
Why it's wrong here
Throttling may drop requests, not improve availability.
Common exam traps
The trap here is that candidates often confuse auto-scaling configuration (cooldown periods, thresholds) with raw capacity planning, assuming that tuning scaling parameters alone can handle sudden spikes, when in fact the instance must have enough headroom to survive the scaling latency.
Detailed technical explanation
Under the hood, SageMaker real-time endpoints use a multi-model container architecture where each instance runs a model server (e.g., TorchServe, TensorFlow Serving, or MMS) that handles inference requests. When CPU utilization hits 100%, the model server's worker threads become saturated, causing request queuing and eventual timeouts (HTTP 504) or connection resets (HTTP 502). The ml.c5.2xlarge instance provides 8 vCPUs and 16 GiB memory compared to the ml.m5.large's 2 vCPUs and 8 GiB, offering a 4x increase in compute capacity, which directly reduces the probability of resource exhaustion during traffic bursts. In real-world scenarios, auto-scaling policies typically have a 1-5 minute cooldown and rely on CloudWatch metrics, which lag behind actual traffic by at least 60 seconds — insufficient for sub-minute traffic spikes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the instance type to ml.c5.2xlarge. — The correct answer is A because upgrading the instance type from ml.m5.large to ml.c5.2xlarge provides more CPU and memory resources, which directly addresses the root cause of 5xx errors during traffic spikes — insufficient compute capacity to handle the request load. Automatic scaling based on CPU utilization may not react quickly enough to sudden spikes, leading to request queuing and timeouts that manifest as 5xx errors. A larger instance type increases the baseline throughput, reducing the likelihood of resource exhaustion before scaling can take effect.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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