The answer is a SageMaker real-time endpoint with multiple instances behind a load balancer. This deployment type is specifically engineered for low latency and high throughput inference, as it keeps the model warm and ready to respond, and horizontal scaling across instances directly handles the required 1000 transactions per second while maintaining sub-200ms response times. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to match deployment options to latency and throughput constraints, with a common trap being to choose serverless inference for its cost appeal—but cold starts and concurrency limits make it unreliable for strict latency under high load. Remember the memory tip: real-time for “right now” latency, batch for “later,” and serverless for “sporadic.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 financial services company is deploying a machine learning model for credit risk assessment. The model must have an inference latency under 200ms and must be able to handle up to 1000 transactions per second (TPS). The company wants to minimize costs. The model is a gradient boosting model implemented in XGBoost. Which SageMaker deployment option should the team choose?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.
SageMaker real-time endpoints are designed for low-latency, high-throughput inference. They can scale horizontally to handle TPS requirements. SageMaker Batch Transform (B) is for offline processing. SageMaker Serverless Inference (C) has cold starts and may not meet latency requirements under high load. SageMaker asynchronous inference (D) is for near-real-time but has higher 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 SageMaker Batch Transform to process transactions in batches.
Why it's wrong here
Batch Transform is for async batch processing, not real-time.
✗
Use SageMaker asynchronous inference for queued requests.
Why it's wrong here
Asynchronous inference has higher latency due to queuing.
✓
Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.
Why this is correct
Real-time endpoints provide sub-second latency and can scale to 1000 TPS.
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.
✗
Use SageMaker Serverless Inference for automatic scaling.
Why it's wrong here
Serverless can have cold starts and may not handle 1000 TPS with low latency.
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.
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.
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 the model on a SageMaker real-time endpoint with multiple instances behind a load balancer. — SageMaker real-time endpoints are designed for low-latency, high-throughput inference. They can scale horizontally to handle TPS requirements. SageMaker Batch Transform (B) is for offline processing. SageMaker Serverless Inference (C) has cold starts and may not meet latency requirements under high load. SageMaker asynchronous inference (D) is for near-real-time but has higher 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.
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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Question Discussion
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