Quick Answer
The correct actions are to enable auto-scaling on the endpoint and to switch to a larger GPU instance like ml.p3.8xlarge. Auto-scaling dynamically adjusts the number of instances based on traffic, which is essential for handling a 5x increase in scaling SageMaker endpoint traffic without manual intervention, while upgrading to a larger instance with more GPUs directly increases throughput per request, maintaining low latency for the deep neural network. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of real-time inference optimization—specifically that GPU instances are non-negotiable for latency-sensitive models, and that scaling policies must be paired with instance sizing. A common trap is assuming adding more instances alone suffices, but without auto-scaling, costs spike and traffic spikes can overwhelm fixed resources. Remember the mnemonic “GPU + Scale” to recall that both compute power and elasticity are required for variable traffic.
This MLS-C01 practice question tests your understanding of modeling. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 using Amazon SageMaker to deploy a model for real-time inference. The model is a deep neural network that requires GPU for low latency. The endpoint currently uses a single ml.p3.2xlarge instance. Traffic is expected to increase by 5x. Which TWO actions should the company take to handle the increased traffic?
Use a larger instance type with more GPUs
Larger instance provides more GPU compute.
Switch to a CPU-based instance
Why wrong: CPU would increase latency for DNN.
Enable auto-scaling on the endpoint
Auto-scaling adjusts capacity based on traffic.
Use a multi-model endpoint
Why wrong: Not relevant for single model.
Decrease the batch size
Why wrong: Reduces throughput.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a larger instance type with more GPUs
Option B is correct because enabling auto-scaling allows the endpoint to handle variable traffic. Option D is correct because using a larger instance with more GPUs (e.g., ml.p3.8xlarge) can increase throughput. Option A is wrong because switching to CPU would increase latency. Option C is wrong because adding more instances without scaling policy may not be optimal. Option E is wrong because reducing batch size would decrease throughput.
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.
Use a larger instance type with more GPUs
Why this is correct
Larger instance provides more GPU compute.
Related concept
Read the scenario before looking for a memorised answer.
Switch to a CPU-based instance
Why it's wrong here
CPU would increase latency for DNN.
Enable auto-scaling on the endpoint
Why this is correct
Auto-scaling adjusts capacity based on traffic.
Related concept
Read the scenario before looking for a memorised answer.
Use a multi-model endpoint
Why it's wrong here
Not relevant for single model.
Decrease the batch size
Why it's wrong here
Reduces throughput.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use a larger instance type with more GPUs — Option B is correct because enabling auto-scaling allows the endpoint to handle variable traffic. Option D is correct because using a larger instance with more GPUs (e.g., ml.p3.8xlarge) can increase throughput. Option A is wrong because switching to CPU would increase latency. Option C is wrong because adding more instances without scaling policy may not be optimal. Option E is wrong because reducing batch size would decrease throughput.
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.
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 →
Same concept, more angles
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 Amazon SageMaker to deploy a real-time inference endpoint for a regression model. The endpoint is experiencing high latency during spikes in traffic. The data scientist needs to reduce latency while maintaining cost efficiency. Which action should the data scientist take?
mediumWhy D: Option D is correct because enabling automatic scaling for the SageMaker endpoint allows the number of instances to dynamically adjust based on traffic patterns, reducing latency during spikes by adding capacity when needed and removing it during low traffic to maintain cost efficiency. Automatic scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or CPUUtilization) to trigger scale-out and scale-in policies, ensuring the endpoint can handle bursts without over-provisioning.
Variation 2. 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?
mediumWhy A: 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.
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Last reviewed: Jun 20, 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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