The recommended best practices are splitting the data into training, validation, and test sets, and handling missing values. Splitting the data is essential because it allows you to evaluate your model’s performance on unseen data, preventing overfitting and ensuring generalization—a standard requirement when using SageMaker’s built-in algorithms or training jobs. Handling missing values is equally critical, as they can introduce bias or cause algorithms to fail; techniques like imputation or removal are common preprocessing steps in AWS data pipelines. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this topic tests your understanding of foundational data preparation for ML workflows, often appearing in scenario-based questions where you must identify which steps prevent data leakage or model degradation. A common trap is forgetting to create a separate validation set for hyperparameter tuning, or assuming missing values can be ignored. Memory tip: think “Split to fit, fix the missing bit”—always partition your data and address gaps before training.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 TWO actions are recommended best practices when preparing training data for a machine learning model in AWS? (Choose two.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check for and handle missing values appropriately.
Option C is correct because missing values can introduce bias or cause algorithms to fail, so handling them (e.g., via imputation or removal) is a critical data preparation step in AWS SageMaker. Option D is correct because splitting data into training, validation, and test sets allows you to evaluate model performance on unseen data and prevent overfitting, which is a standard practice in SageMaker's built-in algorithms and training jobs.
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.
✗
Remove all outliers from the dataset.
Why it's wrong here
Outliers may contain valuable information; blind removal is not recommended.
✗
Train the model on the entire dataset to maximize data usage.
Why it's wrong here
No held-out data leads to overfitting and inability to evaluate.
✓
Check for and handle missing values appropriately.
Why this is correct
Missing values can cause errors or bias if not addressed.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Split the data into training, validation, and test sets.
Why this is correct
Proper splits are crucial for unbiased performance estimation.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Always normalize all features to a [0,1] range.
Why it's wrong here
Normalization is not needed for all algorithms (e.g., decision trees).
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume all outliers must be removed (Option A) or that normalization is always required (Option E), but the exam tests nuanced understanding that these steps depend on the algorithm and data characteristics, not blanket rules.
Detailed technical explanation
How to think about this question
In AWS SageMaker, the built-in algorithms like XGBoost handle missing values natively (e.g., by learning the best direction for splits), but custom preprocessing in SageMaker Processing jobs often uses scikit-learn's SimpleImputer or pandas for missing value strategies. The train/validation/test split is critical for SageMaker's automatic model tuning (hyperparameter optimization), where the validation set is used to evaluate candidate hyperparameters, and the test set provides a final unbiased estimate of model performance.
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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Check for and handle missing values appropriately. — Option C is correct because missing values can introduce bias or cause algorithms to fail, so handling them (e.g., via imputation or removal) is a critical data preparation step in AWS SageMaker. Option D is correct because splitting data into training, validation, and test sets allows you to evaluate model performance on unseen data and prevent overfitting, which is a standard practice in SageMaker's built-in algorithms and training jobs.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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