- A
In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column.
Filtering removes invalid ages, standardizing codes ensures consistency, and oversampling addresses imbalance.
- B
In AWS Glue DataBrew: 1) Impute age with the mean. 2) Apply Standard Scaler to all numeric columns. 3) Use Random Oversampling to balance the target column.
Why wrong: Imputing with mean does not remove invalid values like negative ages, and Standard Scaler may not be appropriate for all numeric columns.
- C
In AWS Glue DataBrew: 1) Replace age with median. 2) Convert diagnosis_code to uppercase. 3) Apply SMOTE to balance the target column.
Why wrong: Replacing with median does not remove invalid values, and SMOTE may create synthetic samples that are not realistic for categorical data.
- D
In AWS Glue DataBrew: 1) Remove rows where age is outside 0-120. 2) Drop diagnosis_code column. 3) Use Random Undersampling to balance the target column.
Why wrong: Dropping diagnosis_code loses potentially important information, and undersampling reduces sample size.
Quick Answer
The correct combination of steps is to filter age between 0 and 120, standardize diagnosis_code to uppercase using a formula, and apply Random Oversampling to balance the target column. This works because AWS Glue DataBrew provides built-in transforms for each task: the filter step removes invalid age values like 150 or negative numbers, the formula-based transformation normalizes inconsistent diagnosis codes such as 'e11' to 'E11', and the Random Oversampling ML transform directly addresses the 60/40 class imbalance in the readmitted column without requiring custom code. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to match data quality issues with DataBrew’s specific capabilities—a common trap is attempting to use a Join or Pivot transform for cleaning, which are irrelevant here. Remember the mnemonic “F-S-O” for Filter, Standardize, Oversample to quickly recall the correct sequence for healthcare readmission data cleaning.
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 startup is building a model to predict patient readmission within 30 days. The data is stored in Amazon Redshift and includes patient demographics, admission history, lab results, and medication records. The data scientist extracts a sample of 10,000 records to Amazon S3 as CSV files for initial prototyping. During exploratory data analysis, they find that the 'age' column has values like '150', '0', and negative numbers. The 'diagnosis_code' column contains codes like 'E11', 'E11.9', and 'e11' (inconsistent formatting). The 'readmitted' target column has 60% 'Yes' and 40% 'No'. The data scientist wants to use AWS Glue DataBrew for data cleaning. Which combination of steps should they use?
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
In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column.
Option A is correct because it uses AWS Glue DataBrew's built-in capabilities to filter invalid age values (0–120), standardize the diagnosis_code to uppercase via a formula, and apply Random Oversampling to address the 60/40 class imbalance. DataBrew supports filtering, formula-based transformations, and built-in ML transforms like Random Oversampling, making this combination valid and efficient for data cleaning.
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.
- ✓
In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column.
Why this is correct
Filtering removes invalid ages, standardizing codes ensures consistency, and oversampling addresses imbalance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
In AWS Glue DataBrew: 1) Impute age with the mean. 2) Apply Standard Scaler to all numeric columns. 3) Use Random Oversampling to balance the target column.
Why it's wrong here
Imputing with mean does not remove invalid values like negative ages, and Standard Scaler may not be appropriate for all numeric columns.
- ✗
In AWS Glue DataBrew: 1) Replace age with median. 2) Convert diagnosis_code to uppercase. 3) Apply SMOTE to balance the target column.
Why it's wrong here
Replacing with median does not remove invalid values, and SMOTE may create synthetic samples that are not realistic for categorical data.
- ✗
In AWS Glue DataBrew: 1) Remove rows where age is outside 0-120. 2) Drop diagnosis_code column. 3) Use Random Undersampling to balance the target column.
Why it's wrong here
Dropping diagnosis_code loses potentially important information, and undersampling reduces sample size.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume SMOTE or Standard Scaler are available in DataBrew, but AWS Glue DataBrew has a limited set of built-in ML transforms (e.g., Random Oversampling, Random Undersampling) and does not include SMOTE or Standard Scaler, which are typically handled in Amazon SageMaker or custom scripts.
Detailed technical explanation
How to think about this question
AWS Glue DataBrew uses Apache Spark under the hood for distributed data processing, enabling efficient filtering and formula-based transformations on large datasets. The Random Oversampling transform in DataBrew duplicates minority class samples to achieve balance, which is suitable for prototyping but can lead to overfitting if not validated properly. For production, techniques like SMOTE or cost-sensitive learning are often preferred, but DataBrew's built-in oversampling is a quick fix for initial model exploration.
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: In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column. — Option A is correct because it uses AWS Glue DataBrew's built-in capabilities to filter invalid age values (0–120), standardize the diagnosis_code to uppercase via a formula, and apply Random Oversampling to address the 60/40 class imbalance. DataBrew supports filtering, formula-based transformations, and built-in ML transforms like Random Oversampling, making this combination valid and efficient for data cleaning.
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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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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