- A
Use SageMaker Batch Transform with multiple instances
Why wrong: Batch Transform is for offline inference, not real-time requests.
- B
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
MME allows multiple models to share a container, reducing cost while scaling to meet demand.
- C
Deploy on a single ml.c5.xlarge instance with a real-time endpoint
Why wrong: A single instance may not handle 1000 req/s with low latency; also cost may be higher if overprovisioned.
- D
Deploy separate real-time endpoints for each model on ml.m5.large instances
Why wrong: Separate endpoints increase cost and management overhead without performance benefit.
Question 149 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 SageMaker Multi-Model Endpoint (MME) Practice Question
This MLA-C01 practice question tests your understanding of sagemaker multi-model endpoint (mme). This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: sageMaker Multi-Model Endpoint (MME). 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 team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?
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 a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
Option B is the most cost-effective because using a Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance allows the single model to be deployed on a larger, more powerful instance that can handle the required throughput and latency. Although MME is designed for multiple models, it also supports single-model deployments, and the ml.c5.4xlarge provides ample compute (16 vCPUs, 32 GB RAM) to achieve under 10 ms latency per request and handle 1000 requests per second. Auto scaling ensures the endpoint adjusts to traffic variations without over-provisioning, making it more cost-effective than using multiple smaller instances or a single smaller instance that may not meet the performance requirements.
Key principle: SageMaker Multi-Model Endpoint (MME)
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 SageMaker Batch Transform with multiple instances
Why it's wrong here
Batch Transform is for offline inference, not real-time requests.
- ✓
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
Why this is correct
MME allows multiple models to share a container, reducing cost while scaling to meet demand.
Related concept
SageMaker Multi-Model Endpoint (MME)
- ✗
Deploy on a single ml.c5.xlarge instance with a real-time endpoint
Why it's wrong here
A single instance may not handle 1000 req/s with low latency; also cost may be higher if overprovisioned.
- ✗
Deploy separate real-time endpoints for each model on ml.m5.large instances
Why it's wrong here
Separate endpoints increase cost and management overhead without performance benefit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates may think a Multi-Model Endpoint is only beneficial when hosting multiple models, but in this scenario, using a single larger instance via MME is more cost-effective than multiple smaller instances, and MME's auto scaling capability helps meet the throughput requirement without over-provisioning.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MME uses a shared inference container that loads models from Amazon S3 on demand, caching them in memory to reduce cold-start latency. For XGBoost, the model size is typically small (a few MB), so multiple models can coexist on one instance without significant memory contention. In real-world scenarios, auto scaling policies should be based on the 'InvocationsPerInstance' CloudWatch metric to ensure the endpoint scales before latency degrades beyond 10 ms.
KKey Concepts to Remember
- SageMaker Multi-Model Endpoint (MME)
- Auto Scaling
- Real-time Inference
- Instance Sizing
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
SageMaker Multi-Model Endpoint (MME)
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 sageMaker Multi-Model Endpoint (MME), then practise related MLA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
SageMaker Multi-Model Endpoint (MME)
What is the correct answer to this question?
The correct answer is: Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling — Option B is the most cost-effective because using a Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance allows the single model to be deployed on a larger, more powerful instance that can handle the required throughput and latency. Although MME is designed for multiple models, it also supports single-model deployments, and the ml.c5.4xlarge provides ample compute (16 vCPUs, 32 GB RAM) to achieve under 10 ms latency per request and handle 1000 requests per second. Auto scaling ensures the endpoint adjusts to traffic variations without over-provisioning, making it more cost-effective than using multiple smaller instances or a single smaller instance that may not meet the performance requirements.
What should I do if I get this MLA-C01 question wrong?
Review sageMaker Multi-Model Endpoint (MME), then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMaker Multi-Model Endpoint (MME)
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Last reviewed: Jun 24, 2026
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