- A
Create a custom Docker image extending the SageMaker scikit-learn container
Extending the container with the custom preprocessing is straightforward and supported.
- B
Package the code in a Lambda layer and use SageMaker hosting
Why wrong: Lambda layers are not directly compatible with SageMaker containers.
- C
Use SageMaker Batch Transform with a custom processing script
Why wrong: Batch Transform is for batch inference, not real-time.
- D
Use SageMaker Neo to compile the model and add preprocessing
Why wrong: Neo is for model optimization, not for adding custom preprocessing code.
Question 1,298 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 wants to serve a scikit-learn model via SageMaker. The inference code requires a custom preprocessing step that is not in the default scikit-learn container. What is the simplest way to deploy?
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 image extending the SageMaker scikit-learn container
Option A is correct because extending the SageMaker scikit-learn container with a custom Docker image is the simplest and most direct way to add custom preprocessing logic that is not included in the default container. SageMaker's pre-built scikit-learn container supports only standard scikit-learn inference code; any additional dependencies or custom preprocessing steps require you to build a custom image that inherits from the official SageMaker scikit-learn image and adds your code. This approach avoids the complexity of managing separate inference pipelines or external services.
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.
- ✓
Create a custom Docker image extending the SageMaker scikit-learn container
Why this is correct
Extending the container with the custom preprocessing is straightforward and supported.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Package the code in a Lambda layer and use SageMaker hosting
Why it's wrong here
Lambda layers are not directly compatible with SageMaker containers.
- ✗
Use SageMaker Batch Transform with a custom processing script
Why it's wrong here
Batch Transform is for batch inference, not real-time.
- ✗
Use SageMaker Neo to compile the model and add preprocessing
Why it's wrong here
Neo is for model optimization, not for adding custom preprocessing code.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SageMaker's built-in algorithm containers with the ability to inject arbitrary code via environment variables or Lambda layers, when in fact custom preprocessing requires a custom Docker image that extends the official container.
Detailed technical explanation
How to think about this question
When you extend the SageMaker scikit-learn container, you create a Dockerfile that starts with `FROM 763104351884.dkr.ecr.<region>.amazonaws.com/sagemaker-scikit-learn:<version>-cpu-py3`, then install additional Python packages (e.g., `nltk`, `spacy`) and copy your custom `inference.py` script that overrides the `input_fn` or `transform_fn` methods. SageMaker automatically invokes these methods during inference, allowing seamless integration of preprocessing. A subtle behavior is that the custom container must expose the same inference API (e.g., `/invocations` endpoint) as the default container, or SageMaker will fail to route requests correctly.
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 image extending the SageMaker scikit-learn container — Option A is correct because extending the SageMaker scikit-learn container with a custom Docker image is the simplest and most direct way to add custom preprocessing logic that is not included in the default container. SageMaker's pre-built scikit-learn container supports only standard scikit-learn inference code; any additional dependencies or custom preprocessing steps require you to build a custom image that inherits from the official SageMaker scikit-learn image and adds your code. This approach avoids the complexity of managing separate inference pipelines or external services.
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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