- A
Deploy to AWS Lambda
Why wrong: SageMaker does not deploy directly to Lambda; you can create a Lambda function to invoke an endpoint.
- B
Deploy to a SageMaker batch transform job
Batch transform processes large batches of data asynchronously.
- C
Deploy to a SageMaker asynchronous endpoint
Asynchronous endpoints handle large payloads with queuing.
- D
Deploy to a SageMaker real-time endpoint
Real-time endpoints provide low-latency inference.
- E
Deploy to Amazon EC2 directly
Why wrong: SageMaker does not deploy directly to EC2; you would need to containerize the model.
Question 208 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.
Which THREE of the following are valid ways to deploy a model using SageMaker? (Select THREE.)
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 to a SageMaker batch transform job
Option B is correct because SageMaker batch transform jobs allow you to run inference on an entire dataset asynchronously, processing large batches of data without requiring a persistent endpoint. This is ideal for offline predictions where low latency is not needed, and the job automatically manages compute resources, scaling, and output storage.
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 to AWS Lambda
Why it's wrong here
SageMaker does not deploy directly to Lambda; you can create a Lambda function to invoke an endpoint.
- ✓
Deploy to a SageMaker batch transform job
Why this is correct
Batch transform processes large batches of data asynchronously.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Deploy to a SageMaker asynchronous endpoint
Why this is correct
Asynchronous endpoints handle large payloads with queuing.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Deploy to a SageMaker real-time endpoint
Why this is correct
Real-time endpoints provide low-latency inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy to Amazon EC2 directly
Why it's wrong here
SageMaker does not deploy directly to EC2; you would need to containerize the model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between SageMaker's managed deployment options (real-time, asynchronous, batch) and external compute services like Lambda or EC2, expecting candidates to recognize that only SageMaker-native endpoints and jobs are considered valid deployment methods within the SageMaker ecosystem.
Detailed technical explanation
How to think about this question
SageMaker batch transform jobs use the same container and model artifacts as real-time endpoints but process data in parallel across multiple instances, writing results to S3. The job automatically handles sharding of input data, retries on failures, and can use different instance types for cost optimization. In practice, batch transform is commonly used for monthly customer churn predictions or nightly fraud scoring where latency is measured in minutes rather than milliseconds.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 to a SageMaker batch transform job — Option B is correct because SageMaker batch transform jobs allow you to run inference on an entire dataset asynchronously, processing large batches of data without requiring a persistent endpoint. This is ideal for offline predictions where low latency is not needed, and the job automatically manages compute resources, scaling, and output storage.
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.
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Last reviewed: Jul 4, 2026
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.
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