- A
SageMaker Multi-model endpoint
Why wrong: Incorrect. SageMaker Multi-model endpoint is designed for hosting multiple independent models on a single endpoint. It is not optimized for a single large ensemble model; a standard real-time endpoint or Serverless Inference is more suitable.
- B
SageMaker Serverless Inference
Correct. SageMaker Serverless Inference provides automatic scaling and is ideal for real-time inference with low latency. For a constantly used model, cold starts are minimal, and the service handles the large ensemble efficiently.
- C
SageMaker Elastic Inference
Why wrong: Incorrect. SageMaker Elastic Inference is designed for deep learning models, not decision tree ensembles. It is not appropriate for this scenario.
- D
SageMaker Batch Transform
Why wrong: Incorrect. SageMaker Batch Transform is for offline, batch inference, not real-time predictions under 100ms.
MLS-C01 Modeling Practice Question
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 machine learning team is deploying a model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?
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
SageMaker Serverless Inference
SageMaker Serverless Inference is the most suitable option because it automatically scales to handle variable traffic and does not require managing underlying infrastructure. Although it may incur cold starts, for a constantly invoked fraud detection model the endpoint remains warm, achieving sub-100ms latency. The large ensemble of decision trees can be deployed as a single model on a Serverless endpoint, which is optimized for real-time inference with low latency and automatic scaling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Multi-model endpoint
Why it's wrong here
Incorrect. SageMaker Multi-model endpoint is designed for hosting multiple independent models on a single endpoint. It is not optimized for a single large ensemble model; a standard real-time endpoint or Serverless Inference is more suitable.
- ✓
SageMaker Serverless Inference
Why this is correct
Correct. SageMaker Serverless Inference provides automatic scaling and is ideal for real-time inference with low latency. For a constantly used model, cold starts are minimal, and the service handles the large ensemble efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Incorrect. SageMaker Elastic Inference is designed for deep learning models, not decision tree ensembles. It is not appropriate for this scenario.
- ✗
SageMaker Batch Transform
Why it's wrong here
Incorrect. SageMaker Batch Transform is for offline, batch inference, not real-time predictions under 100ms.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often select SageMaker Multi-model endpoint (Option A) thinking it is the only real-time option, but it is designed for hosting multiple independent models, not a single large ensemble. A regular real-time endpoint or Serverless Inference is more appropriate. Serverless avoids the overhead of managing instances and can achieve low latency when the endpoint is continuously invoked.
Trap categories for this question
Scenario analysis trap
Incorrect. SageMaker Elastic Inference is designed for deep learning models, not decision tree ensembles. It is not appropriate for this scenario.
Detailed technical explanation
How to think about this question
SageMaker Multi-model endpoints use a model cache on the instance's local SSD or EBS volume, and models are loaded on-demand via a custom inference container that handles routing. The endpoint can serve thousands of models per instance, and the cache eviction policy (LRU) ensures that hot models remain in memory, minimizing latency for frequent inference requests. In practice, for a large ensemble of decision trees, the model size can be kept under 1 GB per model to avoid excessive disk I/O, and the ensemble can be serialized as a single model object to reduce loading overhead.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SageMaker Serverless Inference — SageMaker Serverless Inference is the most suitable option because it automatically scales to handle variable traffic and does not require managing underlying infrastructure. Although it may incur cold starts, for a constantly invoked fraud detection model the endpoint remains warm, achieving sub-100ms latency. The large ensemble of decision trees can be deployed as a single model on a Serverless endpoint, which is optimized for real-time inference with low latency and automatic scaling.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
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 →
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Last reviewed: Jun 24, 2026
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