- A
Drop any duplicate records, apply min-max scaling to all numeric features, and use target encoding for ad_category based on click rates.
Why wrong: Target encoding risks data leakage if not done carefully; min-max scaling may not be optimal for linear learner.
- B
Apply PCA to all numeric and categorical features after converting categories to numeric indices, then standardize the principal components.
Why wrong: PCA on categorical features with arbitrary numeric mapping is inappropriate; converting age to categories loses information.
- C
Apply min-max scaling to customer_age and income, label encode education_level and ad_category, then use recursive feature elimination to reduce dimensionality.
Why wrong: Label encoding on ad_category introduces false ordinal relationships; min-max scaling is not standard for linear models.
- D
Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix.
Standardization helps linear models converge faster; one-hot encoding for categorical with many categories is standard; ordinal encoding preserves the ordinal nature of education.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 marketing company is preparing a dataset to train a logistic regression model to predict whether a customer will click on an online ad. The dataset includes 1 million records with features: customer_age (numeric), income (numeric), education_level (ordinal: high school, bachelor, master, PhD), and ad_category (categorical: 50 unique values). The data is stored in a CSV file in Amazon S3. The data scientist plans to use Amazon SageMaker's built-in linear learner algorithm. The data scientist needs to preprocess the data before training. What is the correct sequence of data preparation steps that should be applied to this dataset to ensure optimal model performance?
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
Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix.
Option D is correct because it applies appropriate preprocessing for a logistic regression model using SageMaker's linear learner. Standardizing numeric features (zero mean, unit variance) is essential for linear models to ensure convergence and equal feature influence. One-hot encoding the categorical ad_category (50 unique values) avoids imposing ordinal relationships, while ordinal encoding education_level respects its natural order. This combination prepares a feature matrix suitable for the linear learner's optimization.
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.
- ✗
Drop any duplicate records, apply min-max scaling to all numeric features, and use target encoding for ad_category based on click rates.
Why it's wrong here
Target encoding risks data leakage if not done carefully; min-max scaling may not be optimal for linear learner.
- ✗
Apply PCA to all numeric and categorical features after converting categories to numeric indices, then standardize the principal components.
Why it's wrong here
PCA on categorical features with arbitrary numeric mapping is inappropriate; converting age to categories loses information.
- ✗
Apply min-max scaling to customer_age and income, label encode education_level and ad_category, then use recursive feature elimination to reduce dimensionality.
Why it's wrong here
Label encoding on ad_category introduces false ordinal relationships; min-max scaling is not standard for linear models.
- ✓
Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix.
Why this is correct
Standardization helps linear models converge faster; one-hot encoding for categorical with many categories is standard; ordinal encoding preserves the ordinal nature of education.
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 choose label encoding for all categorical features (Option C) or target encoding (Option A) without considering the ordinal nature of education_level or the risk of data leakage, leading to suboptimal model performance.
Detailed technical explanation
How to think about this question
SageMaker's linear learner uses stochastic gradient descent, which converges faster when features are standardized (mean=0, variance=1) because it prevents features with larger scales from dominating gradient updates. One-hot encoding a high-cardinality categorical feature (50 categories) creates 50 binary columns, which is acceptable for 1 million records but may require dimensionality reduction if memory is constrained. Ordinal encoding education_level (e.g., high school=1, bachelor=2, master=3, PhD=4) preserves the monotonic relationship expected by logistic regression, unlike label encoding which would assign arbitrary integers.
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 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.
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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: Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix. — Option D is correct because it applies appropriate preprocessing for a logistic regression model using SageMaker's linear learner. Standardizing numeric features (zero mean, unit variance) is essential for linear models to ensure convergence and equal feature influence. One-hot encoding the categorical ad_category (50 unique values) avoids imposing ordinal relationships, while ordinal encoding education_level respects its natural order. This combination prepares a feature matrix suitable for the linear learner's optimization.
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.
About these practice questions
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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