- A
Apply one-hot encoding to both 'product_id' and 'category' columns.
Why wrong: One-hot encoding would create over 50,000 columns, causing memory and performance issues.
- B
Drop the 'category' column and only use 'product_id' since it has more granularity.
Why wrong: Dropping category may reduce model quality as it provides higher-level information.
- C
Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.
Factorization machines natively handle sparse categorical data via feature interactions and do not require one-hot expansion.
- D
Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.
Why wrong: PCA is unsuitable for categorical features and requires dense numeric input.
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 company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?
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
Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.
Option C is correct because Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.
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.
- ✗
Apply one-hot encoding to both 'product_id' and 'category' columns.
Why it's wrong here
One-hot encoding would create over 50,000 columns, causing memory and performance issues.
- ✗
Drop the 'category' column and only use 'product_id' since it has more granularity.
Why it's wrong here
Dropping category may reduce model quality as it provides higher-level information.
- ✓
Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.
Why this is correct
Factorization machines natively handle sparse categorical data via feature interactions and do not require one-hot expansion.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.
Why it's wrong here
PCA is unsuitable for categorical features and requires dense numeric input.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates default to one-hot encoding (Option A) as the standard categorical encoding technique, not realizing that factorization machines are specifically designed to avoid that explosion by accepting raw integer indices as categorical features.
Detailed technical explanation
How to think about this question
Factorization machines work by factorizing pairwise feature interactions into latent vectors, which allows them to learn from sparse high-cardinality data without explicit one-hot expansion. Under the hood, the algorithm expects categorical features as integer indices (e.g., 0 to 49,999 for product_id) and automatically creates a sparse feature matrix, then learns a low-rank interaction matrix via stochastic gradient descent or alternating least squares. In practice, this approach scales linearly with the number of non-zero features, making it ideal for datasets like retail purchase histories where each customer interacts with only a tiny fraction of all products.
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: Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features. — Option C is correct because Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.
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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