- A
Use AWS Glue DataBrew to fill missing values with the median of total_purchases.
Why wrong: Median imputation, like mean imputation, does not preserve the distribution and can introduce bias.
- B
Drop all records with missing total_purchases values.
Why wrong: Dropping 15% of records reduces sample size and may bias the model if missingness is not random.
- C
Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date.
Model-based imputation leverages correlated features to estimate missing values more accurately, reducing bias.
- D
Replace missing total_purchases with the mean of the non-missing values.
Why wrong: Mean imputation reduces variance and can distort relationships between variables.
Quick Answer
The correct approach is to use AWS Glue DynamicFrame for model-based imputation, predicting missing total_purchases values from correlated features like average_order_value and signup_date. This method minimizes bias by leveraging existing data relationships rather than forcing a central tendency like mean or median, which can distort distributions and weaken model performance. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of advanced data preparation techniques within Glue ETL jobs, specifically how to implement custom transformations using Spark MLlib or similar libraries to preserve data integrity. A common trap is choosing simple imputation (mean/median) for speed, but the exam emphasizes bias reduction and pattern preservation. Memory tip: “Model-based imputation maps missing values to their nearest neighbors in feature space—don’t just fill the gap, predict the shape.”
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 retail company is preparing a dataset for a machine learning model to predict customer churn. The dataset includes customer_id, signup_date, last_purchase_date, total_purchases, average_order_value, and churn_label. The data scientist notices that the 'total_purchases' column has missing values for 15% of the records. The company wants to use AWS Glue for data preparation. Which approach should the data scientist take to handle the missing values while minimizing bias and preserving data integrity?
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
Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date.
Option C is correct because model-based imputation uses relationships between features (e.g., average_order_value and signup_date) to predict missing total_purchases values, minimizing bias compared to simple mean/median imputation. AWS Glue DynamicFrames support custom transformation logic, allowing you to implement a predictive model (e.g., using Spark MLlib) directly within the Glue ETL job. This approach preserves data integrity by leveraging existing data patterns rather than discarding records or introducing arbitrary constants.
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.
- ✗
Use AWS Glue DataBrew to fill missing values with the median of total_purchases.
Why it's wrong here
Median imputation, like mean imputation, does not preserve the distribution and can introduce bias.
- ✗
Drop all records with missing total_purchases values.
Why it's wrong here
Dropping 15% of records reduces sample size and may bias the model if missingness is not random.
- ✓
Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date.
Why this is correct
Model-based imputation leverages correlated features to estimate missing values more accurately, reducing bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Replace missing total_purchases with the mean of the non-missing values.
Why it's wrong here
Mean imputation reduces variance and can distort relationships between variables.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose simple imputation (mean/median) or deletion without considering the bias introduced when missing data is not MCAR, and they overlook that AWS Glue DynamicFrames can support custom model-based imputation within the ETL pipeline.
Detailed technical explanation
How to think about this question
Model-based imputation in AWS Glue can be implemented using Spark MLlib's regression algorithms (e.g., LinearRegression or RandomForestRegressor) within a DynamicFrame's apply_mapping or with the Glue Transform library. Under the hood, the imputation model is trained on rows with complete data, then used to predict missing values, preserving the variance and covariance structure of the dataset. In a real-world churn scenario, total_purchases may be correlated with average_order_value, so using that relationship yields more accurate imputations than simple statistics.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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Data Preparation for Machine Learning — study guide chapter
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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: Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date. — Option C is correct because model-based imputation uses relationships between features (e.g., average_order_value and signup_date) to predict missing total_purchases values, minimizing bias compared to simple mean/median imputation. AWS Glue DynamicFrames support custom transformation logic, allowing you to implement a predictive model (e.g., using Spark MLlib) directly within the Glue ETL job. This approach preserves data integrity by leveraging existing data patterns rather than discarding records or introducing arbitrary constants.
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
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