- A
Use stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio.
Stratified sampling ensures that both training and test sets have the same class distribution, which is critical for imbalanced data.
- B
Delete all records with missing values to ensure data integrity.
Why wrong: Deleting 30% of data causes significant information loss and potential bias.
- C
Apply one-hot encoding to all categorical features regardless of cardinality.
Why wrong: One-hot encoding high-cardinality features creates many dummy variables, leading to high dimensionality and overfitting.
- D
Randomly undersample the majority class to balance the dataset before training.
Why wrong: Random undersampling can discard useful majority class examples, reducing model performance; techniques like SMOTE or class weights are preferred.
- E
Use scikit-learn's StandardScaler inside an AWS Glue job to standardize numeric features.
Standardizing numeric features prevents features with larger scales from dominating distance-based models.
Quick Answer
The correct actions are to use scikit-learn's StandardScaler inside an AWS Glue job for standardizing numeric features and to apply stratified sampling when splitting the data. StandardScaler is essential when handling multiple data issues like missing values, imbalance, and scaling because distance-based algorithms such as logistic regression or SVM are sensitive to feature magnitudes; without scaling, features with larger ranges dominate the model. Stratified sampling preserves the 5% positive class proportion in both training and test sets, directly addressing the severe class imbalance without discarding data. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to combine data preparation techniques for a real-world multi-issue dataset, often with a trap that deletes missing rows or one-hot encodes high-cardinality features, which would worsen dimensionality. Remember the mnemonic "Scale and Stratify" — always scale numeric features for distance models and stratify splits to keep rare classes visible.
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.
A data scientist needs to prepare a dataset for a binary classification model. The dataset contains 100,000 records with 50 features, including categorical variables with high cardinality, missing values in 30% of records for a key numeric feature, and a severe class imbalance (5% positive class). The data is stored in an Amazon S3 bucket. Which TWO actions should the data scientist take to improve model performance and ensure robust data preparation? (Choose two.)
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
Use stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio.
Option B is correct because standard scaling is important for distance-based models, and option D is correct because stratified sampling preserves class distribution in train/test split. Option A is wrong because deleting records with missing values would discard 30% of data, leading to loss of information and potential bias. Option C is wrong because one-hot encoding high-cardinality features creates too many dummy variables, causing the curse of dimensionality. Option E is wrong because random undersampling can discard valuable majority class examples, reducing model performance.
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 stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio.
Why this is correct
Stratified sampling ensures that both training and test sets have the same class distribution, which is critical for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Delete all records with missing values to ensure data integrity.
Why it's wrong here
Deleting 30% of data causes significant information loss and potential bias.
- ✗
Apply one-hot encoding to all categorical features regardless of cardinality.
Why it's wrong here
One-hot encoding high-cardinality features creates many dummy variables, leading to high dimensionality and overfitting.
- ✗
Randomly undersample the majority class to balance the dataset before training.
Why it's wrong here
Random undersampling can discard useful majority class examples, reducing model performance; techniques like SMOTE or class weights are preferred.
- ✓
Use scikit-learn's StandardScaler inside an AWS Glue job to standardize numeric features.
Why this is correct
Standardizing numeric features prevents features with larger scales from dominating distance-based models.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Data Preparation for Machine Learning — study guide chapter
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Data Preparation for Machine Learning practice questions
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Use stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio. — Option B is correct because standard scaling is important for distance-based models, and option D is correct because stratified sampling preserves class distribution in train/test split. Option A is wrong because deleting records with missing values would discard 30% of data, leading to loss of information and potential bias. Option C is wrong because one-hot encoding high-cardinality features creates too many dummy variables, causing the curse of dimensionality. Option E is wrong because random undersampling can discard valuable majority class examples, reducing model performance.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
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