The answer is target encoding, which replaces each product ID with the average target value for that product. This approach is most effective for handling high-cardinality categorical features with target encoding in SageMaker Data Wrangler because it preserves predictive power by mapping each category to a single numeric value—the mean of the target—thereby reducing dimensionality from 50,000 unique values to just one column without the feature explosion caused by one-hot encoding. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of categorical encoding trade-offs, often appearing in data preparation scenarios where you must balance model performance with computational efficiency. A common trap is choosing one-hot encoding, which would create 50,000 sparse columns and degrade performance, or label encoding, which imposes an arbitrary ordinal relationship. Remember the memory tip: “Target encoding tames high cardinality by turning categories into target averages.”
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 uses Amazon SageMaker Data Wrangler to create a data flow for a classification model. The dataset contains a high-cardinality categorical feature 'product_id' with 50,000 unique values. The data scientist wants to reduce dimensionality while preserving predictive power. Which approach is most effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Perform target encoding by replacing each product ID with the average target value for that product.
Target encoding is the most effective approach for high-cardinality categorical features because it replaces each category with the mean of the target variable, preserving predictive signal while drastically reducing dimensionality. In SageMaker Data Wrangler, this can be implemented using the 'Encode categorical' transform with the 'Target encoding' option, which avoids the explosion of features caused by one-hot encoding and retains the relationship between product IDs and the target.
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 the 'product_id' column.
Why it's wrong here
50,000 dummy variables lead to extreme dimensionality and risk overfitting.
✓
Perform target encoding by replacing each product ID with the average target value for that product.
Why this is correct
Target encoding condenses information into a single numerical feature while retaining predictive signals.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use feature hashing to map product IDs to a fixed number of buckets (e.g., 100).
Why it's wrong here
Hashing can cause collisions and loss of interpretability.
✗
Drop the 'product_id' column entirely.
Why it's wrong here
The feature may have predictive value; dropping is too aggressive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that feature hashing is always safe for high-cardinality features, but the trap here is that hash collisions can degrade model performance, making target encoding a better choice when the target variable is available and predictive.
Detailed technical explanation
How to think about this question
Target encoding works by calculating the mean of the target variable for each category, often with smoothing (e.g., adding a prior or using cross-validation) to prevent overfitting on rare categories. Under the hood, SageMaker Data Wrangler uses a grouped aggregation step followed by a join to map the encoded values back to the original rows, and it supports regularization via a 'smoothing' parameter that blends the category mean with the global mean. In a real-world scenario, this is critical for features like 'product_id' in retail datasets, where each product has a distinct sales propensity, and target encoding captures that without creating a sparse matrix.
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.
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: Perform target encoding by replacing each product ID with the average target value for that product. — Target encoding is the most effective approach for high-cardinality categorical features because it replaces each category with the mean of the target variable, preserving predictive signal while drastically reducing dimensionality. In SageMaker Data Wrangler, this can be implemented using the 'Encode categorical' transform with the 'Target encoding' option, which avoids the explosion of features caused by one-hot encoding and retains the relationship between product IDs and the target.
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.
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Question Discussion
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