- 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.
MLA-C01 Stratified Sampling 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. A key principle to apply: stratified Sampling. 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 A is correct because stratified sampling preserves the class imbalance ratio (5% positive class) in both training and test sets, ensuring the model is evaluated on a representative distribution and avoiding overfitting to the majority class in training. Option E is correct because standardizing numeric features with scikit-learn's StandardScaler in an AWS Glue job ensures that features have zero mean and unit variance, which is critical for distance-based algorithms (e.g., SVM, k-NN) and many neural networks. Option B is incorrect because deleting records with missing values would discard 30% of data, leading to significant information loss and potential bias, and is especially harmful when missingness is non-random. Option C is incorrect because one-hot encoding high-cardinality categorical features creates a very large number of dummy variables, causing the curse of dimensionality and making the model computationally expensive and prone to overfitting. Option D is incorrect because random undersampling of the majority class discards potentially valuable data, often reducing model performance; techniques like SMOTE or class weights are preferred to address class imbalance.
Key principle: Stratified Sampling
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
Stratified Sampling
- ✗
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
Stratified Sampling
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Stratified Sampling
- StandardScaler
- Handling Missing Data
- High-Cardinality Encoding
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
Stratified Sampling
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Review stratified Sampling, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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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 — Stratified Sampling.
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 A is correct because stratified sampling preserves the class imbalance ratio (5% positive class) in both training and test sets, ensuring the model is evaluated on a representative distribution and avoiding overfitting to the majority class in training. Option E is correct because standardizing numeric features with scikit-learn's StandardScaler in an AWS Glue job ensures that features have zero mean and unit variance, which is critical for distance-based algorithms (e.g., SVM, k-NN) and many neural networks. Option B is incorrect because deleting records with missing values would discard 30% of data, leading to significant information loss and potential bias, and is especially harmful when missingness is non-random. Option C is incorrect because one-hot encoding high-cardinality categorical features creates a very large number of dummy variables, causing the curse of dimensionality and making the model computationally expensive and prone to overfitting. Option D is incorrect because random undersampling of the majority class discards potentially valuable data, often reducing model performance; techniques like SMOTE or class weights are preferred to address class imbalance.
What should I do if I get this MLA-C01 question wrong?
Review stratified Sampling, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Stratified Sampling
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Last reviewed: Jun 23, 2026
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