- A
Use a SageMaker notebook instance as an endpoint.
Why wrong: Notebook instances are not designed for production endpoints.
- B
Create a custom Docker container and deploy to SageMaker endpoint.
Custom container provides flexibility for custom code and dependencies.
- C
Use a built-in SageMaker algorithm.
Why wrong: Built-in algorithms have fixed code; cannot add custom inference.
- D
Use a SageMaker batch transform job.
Why wrong: Batch transform is for offline predictions, not real-time.
Question 1,315 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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 machine learning engineer needs to deploy a model that requires custom inference code with dependencies. Which SageMaker deployment option should be used?
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
✓
Create a custom Docker container and deploy to SageMaker endpoint.
Option B is correct because when a model requires custom inference code with dependencies, the only way to fully control the runtime environment, libraries, and inference logic is to package everything into a custom Docker container. SageMaker endpoints can then deploy this container, allowing the engineer to specify the exact inference script and dependencies (e.g., via a Dockerfile and a requirements.txt). This approach supports any framework or custom logic that built-in algorithms cannot provide.
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 SageMaker notebook instance as an endpoint.
Why it's wrong here
Notebook instances are not designed for production endpoints.
- ✓
Create a custom Docker container and deploy to SageMaker endpoint.
Why this is correct
Custom container provides flexibility for custom code and dependencies.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a built-in SageMaker algorithm.
Why it's wrong here
Built-in algorithms have fixed code; cannot add custom inference.
- ✗
Use a SageMaker batch transform job.
Why it's wrong here
Batch transform is for offline predictions, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake is assuming a SageMaker notebook instance can be used as an inference endpoint, but notebook instances are for development and experimentation only. To serve custom inference code, you must package it in a Docker container and deploy it to a SageMaker endpoint.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints use the Amazon Elastic Container Registry (ECR) to store custom Docker images, and the inference container must implement a web server (e.g., using Flask or Gunicorn) that listens on port 8080 and responds to POST requests with the /invocations path. A subtle behavior is that the container must also handle the /ping endpoint for health checks; failure to implement either correctly will cause the endpoint to fail deployment. In a real-world scenario, a team needing to deploy a PyTorch model with custom preprocessing (e.g., using OpenCV and spaCy) would build a Docker image with those dependencies, push it to ECR, and create a SageMaker model and endpoint configuration referencing that image.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Create a custom Docker container and deploy to SageMaker endpoint. — Option B is correct because when a model requires custom inference code with dependencies, the only way to fully control the runtime environment, libraries, and inference logic is to package everything into a custom Docker container. SageMaker endpoints can then deploy this container, allowing the engineer to specify the exact inference script and dependencies (e.g., via a Dockerfile and a requirements.txt). This approach supports any framework or custom logic that built-in algorithms cannot provide.
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
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