This MLA-C01 practice question tests your understanding of ml model development. 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 data scientist is training a regression model in Amazon SageMaker. The dataset contains missing values in several features. The scientist wants to handle missing values as part of the training pipeline to ensure consistency between training and inference. Which approach should the scientist use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline.
Option D is correct because it uses a scikit-learn container within SageMaker to create a preprocessing step that imputes missing values, then includes that step in the inference pipeline. This ensures the same imputation logic (e.g., mean, median, or custom strategy) is applied consistently during both training and inference, preventing data drift and maintaining reproducibility. SageMaker Pipelines or the built-in scikit-learn container allow the preprocessing to be serialized as part of the model artifact, so inference requests automatically undergo the same transformation.
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.
✗
Impute missing values in a separate Jupyter notebook and save the cleaned data.
Why it's wrong here
Inconsistent between training and inference.
✗
Use SageMaker Autopilot to automatically handle missing values.
Why it's wrong here
Autopilot may not allow custom logic.
✗
Drop all rows with missing values before training.
Why it's wrong here
Dropping rows loses data.
✓
Use a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline.
Why this is correct
Consistent preprocessing in pipeline.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume SageMaker Autopilot (Option B) is the correct choice because it automates preprocessing, but they miss that the question specifically requires a custom, reproducible pipeline that ensures consistency between training and inference, which Autopilot does not expose for custom control.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker's scikit-learn container uses a custom inference script (e.g., `inference.py`) that loads a fitted imputer (like `sklearn.impute.SimpleImputer`) along with the model. During training, the imputer is fit on the training data and serialized (e.g., via joblib) as part of the model.tar.gz. During inference, the endpoint deserializes both the imputer and the model, applying the same imputation transformation to incoming requests. A real-world scenario where this matters is when a feature like 'age' has missing values in production; if the imputation strategy (e.g., median) is not consistently applied, the model's predictions can become unreliable.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline. — Option D is correct because it uses a scikit-learn container within SageMaker to create a preprocessing step that imputes missing values, then includes that step in the inference pipeline. This ensures the same imputation logic (e.g., mean, median, or custom strategy) is applied consistently during both training and inference, preventing data drift and maintaining reproducibility. SageMaker Pipelines or the built-in scikit-learn container allow the preprocessing to be serialized as part of the model artifact, so inference requests automatically undergo the same transformation.
What should I do if I get this MLA-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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Question Discussion
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