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Data Preparation for Machine Learning →mediumMultiple ChoiceObjective-mapped

MLA-C01 Data Preparation for Machine Learning 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. 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 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?

Question 1mediummultiple choice

Read the full NAT/PAT explanation →

A
Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.
Why wrong: Mean imputation is sensitive to outliers; label encoding is fine but missing imputation method could be better.
B
Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.
Median is robust; integer encoding is sufficient for tree-based models like XGBoost.
C
Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.
Why wrong: Replacing with -1 introduces arbitrary value; one-hot still large even after grouping.
D
Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.
Why wrong: One-hot encoding creates too many columns; dropping rows loses data.

Full breakdown with real-world context →

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

✓

Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.

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.

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.

✗
Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.
Why it's wrong here
Mean imputation is sensitive to outliers; label encoding is fine but missing imputation method could be better.
✓
Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.
Why this is correct
Median is robust; integer encoding is sufficient for tree-based models like XGBoost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.
Why it's wrong here
Replacing with -1 introduces arbitrary value; one-hot still large even after grouping.
✗
Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.
Why it's wrong here
One-hot encoding creates too many columns; dropping rows loses data.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates overestimate the need for one-hot encoding with high-cardinality categorical features, forgetting that tree-based models like XGBoost can effectively use integer encoding, and they may also default to mean imputation without considering outlier sensitivity.

Detailed technical explanation

How to think about this question

XGBoost's sparsity-aware algorithm learns the best direction to handle missing values during training by default, so explicit imputation is often unnecessary, but median imputation is a safe practice when preprocessing must produce a complete dataset for downstream pipelines. Integer encoding assigns a unique integer to each diagnosis code without implying order, which is acceptable for tree-based models because splits are based on threshold comparisons, not distance metrics. In AWS Glue, using `DynamicFrame` with `ApplyMapping` or `DropNullFields` can efficiently handle these transformations at scale across 2 million rows.

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.

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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: Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'. — 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.

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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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.