- A
Use a real-time endpoint with a single model
Real-time endpoints provide low-latency inference.
- B
Use a serverless inference endpoint
Why wrong: Serverless has cold starts and may exceed latency requirements.
- C
Use a real-time endpoint with multi-model hosting
Why wrong: Multi-model may introduce latency due to model loading.
- D
Use a batch transform job
Why wrong: Batch transform is for offline, not real-time.
- E
Use an asynchronous inference endpoint
Why wrong: Asynchronous is for near-real-time with higher latency.
Question 1,353 of 1,755
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 company is deploying a machine learning model to production on Amazon SageMaker. The model requires low-latency inference (under 10 ms) for real-time predictions. The data scientist has trained a model using XGBoost and wants to minimize cost while meeting latency requirements. Which SageMaker hosting option should be used?
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 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 real-time endpoint with a single model
A real-time endpoint with a single model is the correct choice because it provides dedicated, always-on compute resources that can consistently achieve sub-10 ms inference latency for XGBoost models. SageMaker real-time endpoints keep instances warm and route requests directly to the model container, minimizing cold-start delays and network overhead, which is essential for low-latency 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.
- ✓
Use a real-time endpoint with a single model
Why this is correct
Real-time endpoints provide low-latency inference.
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 a serverless inference endpoint
Why it's wrong here
Serverless has cold starts and may exceed latency requirements.
- ✗
Use a real-time endpoint with multi-model hosting
Why it's wrong here
Multi-model may introduce latency due to model loading.
- ✗
Use a batch transform job
Why it's wrong here
Batch transform is for offline, not real-time.
- ✗
Use an asynchronous inference endpoint
Why it's wrong here
Asynchronous is for near-real-time with higher latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'serverless' with 'low-latency' because serverless is cost-effective, but they overlook the cold-start penalty that makes it unsuitable for sub-10 ms inference; AWS often tests this by pairing a latency requirement with a cost-saving option to see if you prioritize performance constraints over cost optimization.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use an auto-scaling policy based on invocation metrics and maintain a persistent container runtime, which avoids the cold-start overhead of serverless options. For XGBoost, the model size is typically small (a few MB), so the single-model endpoint can keep the model in memory and serve predictions via the SageMaker InvokeEndpoint API with HTTP/2 or gRPC for minimal latency. In production, you would also configure a small instance type (e.g., ml.m5.large) and enable data capture for monitoring, ensuring cost efficiency while meeting the 10 ms SLA.
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 a real-time endpoint with a single model — A real-time endpoint with a single model is the correct choice because it provides dedicated, always-on compute resources that can consistently achieve sub-10 ms inference latency for XGBoost models. SageMaker real-time endpoints keep instances warm and route requests directly to the model container, minimizing cold-start delays and network overhead, which is essential for low-latency 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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Last reviewed: Jul 4, 2026
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