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Question 786 of 1,755

Machine Learning Implementation and Operations →hardMultiple ChoiceObjective-mapped

Deploying a GPU-Powered Real-Time Endpoint with Multi-AZ High Availability in SageMaker

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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. 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 data scientist is deploying a real-time inference endpoint using SageMaker. The model is a large NLP model requiring GPU for low latency. The endpoint must be highly available across two Availability Zones. Which deployment configuration meets these requirements?

A
Deploy a single model endpoint on an ml.c5.xlarge instance with auto-scaling
Why wrong: CPU instance does not meet GPU requirement.
B
Use SageMaker batch transform on GPU instances
Why wrong: Batch transform is not real-time inference.
C
Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs
GPU, auto-scaling, and multi-AZ provide low latency and high availability.
D
Deploy a single model endpoint on an ml.p3.2xlarge instance with one instance
Why wrong: Single instance fails if AZ goes down.

Full breakdown with real-world context →

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

✓

Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs

Option C is correct because it uses a GPU instance (ml.p3.2xlarge) to meet the low-latency requirement for a large NLP model, and it deploys at least two instances across different Availability Zones (AZs) to achieve high availability. SageMaker multi-model endpoints allow hosting multiple models on the same endpoint, but here the key is the instance type and the multi-instance, multi-AZ deployment for fault tolerance.

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.

✗
Deploy a single model endpoint on an ml.c5.xlarge instance with auto-scaling
Why it's wrong here
CPU instance does not meet GPU requirement.
✗
Use SageMaker batch transform on GPU instances
Why it's wrong here
Batch transform is not real-time inference.
✓
Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs
Why this is correct
GPU, auto-scaling, and multi-AZ provide low latency and high availability.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy a single model endpoint on an ml.p3.2xlarge instance with one instance
Why it's wrong here
Single instance fails if AZ goes down.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may overlook the GPU requirement and choose a cheaper CPU instance (Option A), or confuse batch transform with real-time inference (Option B), or forget that a single instance cannot provide high availability (Option D).

Detailed technical explanation

How to think about this question

SageMaker real-time endpoints use Elastic Load Balancing (ELB) to distribute traffic across instances in multiple AZs, ensuring fault tolerance. GPU instances like ml.p3.2xlarge (with NVIDIA V100 GPUs) are essential for large transformer models (e.g., BERT, GPT) to achieve sub-second inference latency. Auto-scaling policies based on metrics like InvocationsPerInstance or GPUUtilization can dynamically adjust capacity to handle traffic spikes while maintaining availability.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs — Option C is correct because it uses a GPU instance (ml.p3.2xlarge) to meet the low-latency requirement for a large NLP model, and it deploys at least two instances across different Availability Zones (AZs) to achieve high availability. SageMaker multi-model endpoints allow hosting multiple models on the same endpoint, but here the key is the instance type and the multi-instance, multi-AZ deployment for fault tolerance.

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

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Same concept, more angles

1 more ways this is tested on MLS-C01

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 data science team is deploying a machine learning model using Amazon SageMaker. The model requires GPU inference and must handle variable traffic with low latency. Which TWO options should the team implement to meet these requirements? (Choose TWO.)

medium

✓ A.Use a SageMaker multi-model endpoint with a GPU instance to serve multiple models.
B.Deploy to a SageMaker real-time endpoint using a CPU instance and attach an Elastic Inference accelerator.
C.Use AWS Lambda with an attached GPU function for inference.
D.Host the model on a SageMaker batch transform job with GPU instances.
✓ E.Deploy the model to a SageMaker real-time endpoint using a GPU instance type.

Why A: A is correct because a SageMaker multi-model endpoint with a GPU instance allows you to host multiple models on a single endpoint, dynamically loading and unloading them based on traffic, while providing GPU acceleration for low-latency inference. This approach efficiently handles variable traffic patterns by scaling the endpoint and leveraging GPU compute for deep learning models.

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