- A
Use a multi-model endpoint to host multiple models on the same instance.
Why wrong: Using a multi-model endpoint is for hosting multiple models on the same instance to save costs, not for achieving high throughput for a single model. It can increase latency due to model loading overhead.
- B
Enable data capture to Amazon S3 for model monitoring and retraining.
Why wrong: Enabling data capture to Amazon S3 is for monitoring model performance and retraining. It does not directly affect the endpoint's ability to handle 1000 requests per second or maintain low latency. This is not a performance-scaling action.
- C
Use a serverless inference endpoint to automatically scale.
Why wrong: Serverless inference endpoints automatically scale, but they have cold start latency and may not be suitable for sustained low-latency high-throughput workloads like 1000 req/s. They are better for unpredictable or bursty traffic with lower throughput.
- D
Configure an auto scaling policy for the endpoint based on invocation metrics.
Configuring an auto scaling policy based on invocation metrics (e.g., SageMakerVariantInvocationsPerInstance) ensures the endpoint adds or removes instances dynamically to handle up to 1000 requests per second while maintaining low latency.
- E
Deploy the model on a single large instance (e.g., ml.p3.16xlarge).
Deploying the model on a single large instance (e.g., ml.p3.16xlarge) can provide sufficient compute capacity to handle the full 1000 req/s load without the need for scaling, assuming the instance's throughput capability matches the demand.
Ensuring Low-Latency High Throughput for SageMaker Endpoints
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. A key principle to apply: auto Scaling for SageMaker Endpoints. 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 data scientist is deploying a machine learning model on Amazon SageMaker for real-time inference. The model requires low-latency predictions and must be able to handle up to 1000 requests per second. Which TWO actions should the data scientist take to ensure the endpoint can meet the performance requirements? (Choose 2.)
Answer choices
Why each option matters
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 for the endpoint based on invocation metrics.
Option D is correct because configuring an auto scaling policy based on invocation metrics (e.g., SageMakerVariantInvocationsPerInstance) allows the endpoint to dynamically scale to handle variable loads up to 1000 requests per second while maintaining low latency. Option E is correct because deploying on a single large instance (e.g., ml.p3.16xlarge) can provide enough compute capacity to meet the peak load of 1000 req/s without the complexity of scaling, assuming the instance can handle the throughput. Option B (data capture) is for monitoring and retraining, not directly for meeting latency or throughput requirements. Option A (multi-model endpoint) is designed for cost savings with many small models, not for high-throughput single-model serving. Option C (serverless endpoint) may have cold start delays and is typically not recommended for sustained high-throughput low-latency workloads.
Key principle: Auto Scaling for SageMaker Endpoints
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a multi-model endpoint to host multiple models on the same instance.
Why it's wrong here
Using a multi-model endpoint is for hosting multiple models on the same instance to save costs, not for achieving high throughput for a single model. It can increase latency due to model loading overhead.
- ✗
Enable data capture to Amazon S3 for model monitoring and retraining.
Why it's wrong here
Enabling data capture to Amazon S3 is for monitoring model performance and retraining. It does not directly affect the endpoint's ability to handle 1000 requests per second or maintain low latency. This is not a performance-scaling action.
- ✗
Use a serverless inference endpoint to automatically scale.
Why it's wrong here
Serverless inference endpoints automatically scale, but they have cold start latency and may not be suitable for sustained low-latency high-throughput workloads like 1000 req/s. They are better for unpredictable or bursty traffic with lower throughput.
- ✓
Configure an auto scaling policy for the endpoint based on invocation metrics.
Why this is correct
Configuring an auto scaling policy based on invocation metrics (e.g., SageMakerVariantInvocationsPerInstance) ensures the endpoint adds or removes instances dynamically to handle up to 1000 requests per second while maintaining low latency.
Related concept
Auto Scaling for SageMaker Endpoints
- ✓
Deploy the model on a single large instance (e.g., ml.p3.16xlarge).
Why this is correct
Deploying the model on a single large instance (e.g., ml.p3.16xlarge) can provide sufficient compute capacity to handle the full 1000 req/s load without the need for scaling, assuming the instance's throughput capability matches the demand.
Related concept
Auto Scaling for SageMaker Endpoints
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates often select data capture (B) as a performance-related action, but it is for model monitoring and retraining, not for capacity planning. For performance requirements, focus on scaling (D) and instance selection (E).
Detailed technical explanation
How to think about this question
Under the hood, SageMaker auto scaling uses Application Auto Scaling with target tracking policies based on the 'SageMakerVariantInvocationsPerInstance' metric, which adjusts the number of instances to keep the average invocations per instance at a target value (e.g., 1000). Data capture writes each inference request and response as JSON lines to S3, enabling you to detect data drift using SageMaker Model Monitor, which can trigger retraining pipelines to keep the model accurate and prevent latency degradation from stale predictions.
KKey Concepts to Remember
- Auto Scaling for SageMaker Endpoints
- Instance Selection for Inference
TExam Day Tips
- 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
Auto Scaling for SageMaker Endpoints
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
Review auto Scaling for SageMaker Endpoints, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Auto Scaling for SageMaker Endpoints.
What is the correct answer to this question?
The correct answer is: Configure an auto scaling policy for the endpoint based on invocation metrics. — Option D is correct because configuring an auto scaling policy based on invocation metrics (e.g., SageMakerVariantInvocationsPerInstance) allows the endpoint to dynamically scale to handle variable loads up to 1000 requests per second while maintaining low latency. Option E is correct because deploying on a single large instance (e.g., ml.p3.16xlarge) can provide enough compute capacity to meet the peak load of 1000 req/s without the complexity of scaling, assuming the instance can handle the throughput. Option B (data capture) is for monitoring and retraining, not directly for meeting latency or throughput requirements. Option A (multi-model endpoint) is designed for cost savings with many small models, not for high-throughput single-model serving. Option C (serverless endpoint) may have cold start delays and is typically not recommended for sustained high-throughput low-latency workloads.
What should I do if I get this MLS-C01 question wrong?
Review auto Scaling for SageMaker Endpoints, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Auto Scaling for SageMaker Endpoints
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Last reviewed: Jul 4, 2026
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