The correct technique is target encoding, also known as mean encoding, which replaces each high-cardinality categorical value with the mean of the target variable for that category. This approach is effective because it compresses thousands of unique values—like zip codes—into a single numeric feature that captures the predictive relationship with the target, avoiding the dimensionality explosion of one-hot encoding. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of feature engineering trade-offs in SageMaker, where the trap is choosing hash encoding (which risks collisions) or label encoding (which implies false ordinality). Remember that target encoding works best when you have enough samples per category to produce stable means, and you should always use cross-validation to prevent target leakage. A simple memory tip: “Mean for the many, one-hot for the few.”
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 machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply target encoding (mean encoding) to the high-cardinality features.
For high-cardinality categorical features, target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which captures information without creating a large number of dummy variables. One-hot encoding would create too many features. Label encoding implies ordinal relationships. Hash encoding can cause collisions.
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 hash encoding to map categories to a fixed number of buckets.
Why it's wrong here
Hash encoding may cause collisions and lose information.
✓
Apply target encoding (mean encoding) to the high-cardinality features.
Why this is correct
Target encoding reduces dimensionality and captures target-related information.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply one-hot encoding to all categorical features.
Why it's wrong here
One-hot encoding high-cardinality features creates too many columns, increasing dimensionality.
✗
Apply label encoding to assign integer values to each category.
Why it's wrong here
Label encoding implies an order that may not exist, leading to misleading patterns.
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.
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: Apply target encoding (mean encoding) to the high-cardinality features. — For high-cardinality categorical features, target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which captures information without creating a large number of dummy variables. One-hot encoding would create too many features. Label encoding implies ordinal relationships. Hash encoding can cause collisions.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?
medium
A.Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.
✓ B.Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.
C.Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.
D.Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.
Why B: Option B is correct because XGBoost handles missing values natively, so median imputation for 'blood_pressure' is robust to outliers and preserves data distribution, while integer encoding (label encoding) for 'diagnosis_code' with 10,000 unique values is efficient and avoids the dimensionality explosion of one-hot encoding. AWS Glue ETL can apply these transformations using built-in functions like `Imputer` and `StringIndexer` without excessive memory overhead.
Variation 2. A company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)
hard
A.Remove any rows with outlier values.
✓ B.Split the data into training, validation, and test sets before any imputation.
✓ C.Impute missing numeric values with median or mean.
✓ D.For categorical features, use one-hot encoding for low cardinality and target encoding for high cardinality.
E.Apply target encoding to all categorical features regardless of cardinality.
Why B: Option B is correct because splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new data.
Variation 3. A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?
hard
A.Hash encoding using Apache Spark on Amazon EMR
B.One-hot encoding using SageMaker Processing with scikit-learn
C.Label encoding using Pandas in a SageMaker notebook
✓ D.Target encoding with smoothing using SageMaker Data Wrangler
Why D: Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.
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Question Discussion
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