The answer is to use Amazon SageMaker automatic scaling with a target tracking scaling policy. This is correct because SageMaker’s application auto scaling monitors the SageMakerVariantInvocationsPerInstance metric, which tracks the number of concurrent requests per instance, and automatically adjusts the instance count to maintain a target utilization you define. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how SageMaker handles real-time inference scaling without external components like Elastic Load Balancers or Auto Scaling groups, which are common traps. A key distinction is that SageMaker uses its own built-in load balancer, not an integrated ELB, and target tracking is the native way to scale based on invocations. Memory tip: think “Target Tracking on Invocations Per Instance” — if you see “ELB” or “Auto Scaling group” as options for a SageMaker endpoint, they are almost always distractors.
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 machine learning engineer is deploying a model using Amazon SageMaker. The model is a PyTorch model that performs real-time inference with low latency requirements. The engineer wants to use automatic scaling based on the number of concurrent requests. Which SageMaker feature should be used to achieve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker automatic scaling with a target tracking scaling policy.
Option B is correct because SageMaker's application auto scaling with a target tracking scaling policy based on the SageMakerVariantInvocationsPerInstance metric automatically adjusts the number of instances. Option A is wrong because SageMaker does not have an integrated Elastic Load Balancer; it uses a built-in load balancer. Option C is wrong because SageMaker does not natively support AWS Auto Scaling groups for endpoints. Option D is wrong because Amazon API Gateway is used for REST APIs, not for scaling SageMaker endpoints.
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.
✗
Create an AWS Auto Scaling group for the SageMaker endpoint.
Why it's wrong here
SageMaker endpoints do not use Auto Scaling groups.
✗
Enable Elastic Load Balancing for the endpoint.
Why it's wrong here
SageMaker endpoints have a built-in load balancer; external ELB is not used for scaling.
✓
Use Amazon SageMaker automatic scaling with a target tracking scaling policy.
Why this is correct
This scales based on invocations per instance.
Related concept
Static NAT maps one inside address to one outside address.
✗
Deploy the model behind Amazon API Gateway with a Lambda function.
Why it's wrong here
API Gateway does not manage SageMaker endpoint scaling.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Use Amazon SageMaker automatic scaling with a target tracking scaling policy. — Option B is correct because SageMaker's application auto scaling with a target tracking scaling policy based on the SageMakerVariantInvocationsPerInstance metric automatically adjusts the number of instances. Option A is wrong because SageMaker does not have an integrated Elastic Load Balancer; it uses a built-in load balancer. Option C is wrong because SageMaker does not natively support AWS Auto Scaling groups for endpoints. Option D is wrong because Amazon API Gateway is used for REST APIs, not for scaling SageMaker endpoints.
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
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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 team is deploying a machine learning model to production using Amazon SageMaker. They want to automatically scale the endpoint based on the incoming request volume, and they also need to ensure that the endpoint can handle sudden bursts of traffic without dropping requests. Which scaling policy should they use?
medium
A.Scheduled scaling policy for peak hours
✓ B.Target tracking scaling policy based on the number of invocations
C.Simple scaling policy based on average latency
D.Manual scaling by monitoring CloudWatch alarms
Why B: Option B is correct because a target tracking scaling policy with a specified target value for the metric allows the endpoint to automatically adjust capacity to maintain the target metric, and it can handle bursts by adding more instances proactively. Option A is wrong because a simple scaling policy based on average latency may not handle bursts quickly. Option C is wrong because a scheduled scaling policy is for predictable traffic patterns. Option D is wrong because manual scaling is not automatic.
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Question Discussion
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