- A
Use AWS Lambda to invoke each model sequentially.
Why wrong: Invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement.
- B
Deploy each model as a separate SageMaker endpoint and use a load balancer.
Why wrong: Deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance.
- C
Deploy the ensemble on a single GPU instance with large batch processing.
Why wrong: Batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
- D
Use SageMaker multi-model endpoint on a compute-optimized instance.
A multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints.
Deploying Multiple Models on a SageMaker Multi-Model Endpoint
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 company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The model is an ensemble of 5 gradient boosted trees (XGBoost), each 200 MB. Which deployment strategy 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
Use SageMaker multi-model endpoint on a compute-optimized instance.
Option D is correct because a multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints. Option A is wrong because invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement. Option B is wrong because deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance. Option C is wrong because batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
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.
- ✗
Use AWS Lambda to invoke each model sequentially.
Why it's wrong here
Invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement.
- ✗
Deploy each model as a separate SageMaker endpoint and use a load balancer.
Why it's wrong here
Deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance.
- ✗
Deploy the ensemble on a single GPU instance with large batch processing.
Why it's wrong here
Batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
- ✓
Use SageMaker multi-model endpoint on a compute-optimized instance.
Why this is correct
A multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
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.
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.
- →
Modeling — study guide chapter
Learn the concepts, then practise the questions
- →
Modeling practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use SageMaker multi-model endpoint on a compute-optimized instance. — Option D is correct because a multi-model endpoint on a compute-optimized instance allows loading multiple models dynamically, reducing cost and latency compared to separate endpoints. Option A is wrong because invoking each model sequentially with Lambda would incur overhead and cold start latency, exceeding the 100 ms requirement. Option B is wrong because deploying each model as a separate SageMaker endpoint would require managing multiple endpoints, increasing cost and complexity without benefiting from model sharing on a single instance. Option C is wrong because batch processing is not suitable for real-time inference; GPU instances are overkill for tree-based models and add latency.
What should I do if I get this MLS-C01 question wrong?
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.
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 →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.