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Machine Learning Implementation and Operations →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is Amazon SageMaker Serverless Inference. This service is the correct choice because it is specifically designed for deploying ML models with serverless inference for low latency and scaling to zero, automatically managing infrastructure to handle intermittent traffic patterns. SageMaker Serverless Inference supports models up to 1 GB in size, making it a perfect fit for this 500 MB deep neural network, and it delivers sub-second latency for real-time requests while scaling down to zero when idle to minimize cost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of trade-offs between serverless and real-time endpoints; a common trap is choosing SageMaker real-time endpoints, which do not scale to zero and incur costs even when idle. Remember the memory tip: “Serverless sleeps, real-time keeps”—if the model is under 1 GB and traffic is intermittent, serverless inference is the cost-effective, low-latency answer.

MLS-C01 Practice Question: Machine Learning Implementation and Operations

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. 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 wants to deploy a machine learning model that performs real-time inference with sub-second latency. The model is a deep neural network with 500 MB of weights. The inference endpoint must scale to zero when not in use to minimize cost. Which AWS service should the company use?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1mediummultiple choice

Full question →

A
Deploy the model as an AWS Lambda function with provisioned concurrency.
Why wrong: AWS Lambda has a deployment package limit of 250 MB (including layers), so a 500 MB model cannot be deployed directly. Also, provisioned concurrency does not scale to zero.
B
Use Amazon SageMaker Serverless Inference to host the model.
SageMaker Serverless Inference automatically scales to zero when idle, reducing costs, and can handle sub-second latency for suitable workloads. It also supports large model sizes.
C
Host the model on Amazon ECS with Fargate and use a target tracking scaling policy.
Why wrong: While ECS with Fargate can scale to zero, it requires more operational overhead (e.g., container management, load balancer) and is not optimized for ML inference latency compared to SageMaker Serverless.
D
Create an Amazon SageMaker real-time endpoint with automatic scaling policies.
Why wrong: Real-time endpoints do not scale to zero; they maintain a minimum number of instances, leading to ongoing costs even when not in use.

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

✓

Use Amazon SageMaker Serverless Inference to host the model.

Amazon SageMaker Serverless Inference is designed for workloads with intermittent traffic patterns, automatically scaling to zero when idle and scaling up for real-time requests. It supports models up to 1 GB in size and provides sub-second latency for inference, making it ideal for this 500 MB deep neural network. This service eliminates the need to manage underlying infrastructure while meeting the latency and cost requirements.

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 the model as an AWS Lambda function with provisioned concurrency.
Why it's wrong here
AWS Lambda has a deployment package limit of 250 MB (including layers), so a 500 MB model cannot be deployed directly. Also, provisioned concurrency does not scale to zero.
✓
Use Amazon SageMaker Serverless Inference to host the model.
Why this is correct
SageMaker Serverless Inference automatically scales to zero when idle, reducing costs, and can handle sub-second latency for suitable workloads. It also supports large model sizes.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Host the model on Amazon ECS with Fargate and use a target tracking scaling policy.
Why it's wrong here
While ECS with Fargate can scale to zero, it requires more operational overhead (e.g., container management, load balancer) and is not optimized for ML inference latency compared to SageMaker Serverless.
✗
Create an Amazon SageMaker real-time endpoint with automatic scaling policies.
Why it's wrong here
Real-time endpoints do not scale to zero; they maintain a minimum number of instances, leading to ongoing costs even when not in use.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse SageMaker Serverless Inference with SageMaker real-time endpoints, assuming automatic scaling can reduce costs to zero, but real-time endpoints always require a minimum instance count, whereas Serverless Inference truly scales to zero.

Detailed technical explanation

How to think about this question

SageMaker Serverless Inference uses AWS Lambda under the hood but abstracts the function size and timeout limitations by managing model loading and invocation through a dedicated service. It provisions compute resources on-demand with a concurrency limit per endpoint, and cold starts can occur after idle periods, though the service optimizes for sub-second latency by caching model artifacts in memory. In practice, this service is ideal for bursty, unpredictable traffic patterns, such as a chatbot that receives requests sporadically throughout the day.

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

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.

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: Use Amazon SageMaker Serverless Inference to host the model. — Amazon SageMaker Serverless Inference is designed for workloads with intermittent traffic patterns, automatically scaling to zero when idle and scaling up for real-time requests. It supports models up to 1 GB in size and provides sub-second latency for inference, making it ideal for this 500 MB deep neural network. This service eliminates the need to manage underlying infrastructure while meeting the latency and cost requirements.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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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.