The answer is to configure an auto scaling policy that scales down during low traffic while keeping a minimum of one instance. This directly addresses SageMaker endpoint auto scaling cost reduction by ensuring you only pay for what you need during off-peak hours, while the single warm instance eliminates the 30-second cold start penalty when traffic spikes. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of balancing cost and latency for real-time inference endpoints—a common trap is assuming serverless endpoints are always cheaper, but they suffer from cold starts that can’t handle sudden 500 TPS bursts. Another pitfall is choosing Batch Transform, which is for offline inference, not real-time. Remember the memory tip: “One warm instance beats a cold serverless surprise” to recall that a minimum of one instance prevents latency spikes while scaling down saves money.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 endpoint uses an ml.c5.xlarge instance. The company wants to reduce costs without affecting performance. The current traffic pattern shows a daily peak of 500 requests per second for 2 hours, and the rest of the day sees fewer than 50 requests per second. The model has a cold start time of about 30 seconds. What should the company do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.
Option D is correct because adding a scaling policy to scale down during low traffic reduces cost, and keeping a minimum instance ensures low latency during low traffic without cold starts. Option A is wrong because serverless endpoints have cold starts and may not handle 500 TPS. Option B is wrong because Batch Transform is not for real-time. Option C is wrong because one instance during peak may cause latency.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Switch to a serverless inference endpoint.
Why it's wrong here
Serverless has cold start and max concurrency limits.
✓
Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.
Why this is correct
Auto scaling reduces instances during low traffic, and minimum instance prevents cold starts.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use a single ml.c5.xlarge instance and rely on it.
Why it's wrong here
Single instance may not handle peak load.
✗
Use SageMaker Batch Transform for all predictions.
Why it's wrong here
Batch Transform is for offline, not real-time.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance. — Option D is correct because adding a scaling policy to scale down during low traffic reduces cost, and keeping a minimum instance ensures low latency during low traffic without cold starts. Option A is wrong because serverless endpoints have cold starts and may not handle 500 TPS. Option B is wrong because Batch Transform is not for real-time. Option C is wrong because one instance during peak may cause latency.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 has deployed a machine learning model on Amazon SageMaker for real-time inference. The endpoint uses a single ml.c5.xlarge instance. Recently, the traffic has increased, and the endpoint is returning HTTP 503 (Service Unavailable) errors during peak hours. The CloudWatch metrics show that the CPU utilization is consistently above 90% during peak times, and the Invocations metric shows that requests are being throttled. The data science team has already optimized the model to reduce inference time by 20%, but the errors persist. The company needs to resolve the issue without increasing costs significantly. Which course of action should be taken?
hard
A.Change the instance type to a larger size, such as ml.c5.2xlarge
B.Switch to batch transform to process requests in batches
C.Use spot instances to reduce costs and add more instances
✓ D.Configure auto-scaling for the endpoint to add instances based on CPU utilization
Why D: Option A is correct because adding auto-scaling based on CPU utilization or invocations will dynamically adjust the number of instances to handle the load, reducing errors without incurring costs during low traffic. Option B is wrong because increasing to a larger instance type will increase costs even during low traffic. Option C is wrong because switching to batch transform is for offline processing, not real-time. Option D is wrong because using spot instances could lead to interruptions and does not solve the capacity issue.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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