- A
Use a SageMaker notebook instance with Pandas to load the entire dataset into memory, fill missing values with the median, and drop duplicate customer IDs.
Why wrong: Loading 50 GB into a notebook instance's memory is inefficient and may cause out-of-memory errors; Pandas is not distributed.
- B
Use an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.
Glue is serverless, scales automatically, and is suitable for 50 GB. It can efficiently handle missing value imputation and deduplication.
- C
Drop all records with missing values in the transaction_amount column and remove duplicate customer IDs using an Athena SQL query, then store the result in S3.
Why wrong: Dropping all records with missing values would discard 15% of data, leading to potential bias and loss of information.
- D
Use an Amazon EMR cluster with Spark to read the CSV files, impute missing transaction amounts with the mean or median, and remove duplicate customers.
Why wrong: EMR is unnecessarily complex for a simple fill and dedup operation; AWS Glue is a simpler and cost-effective alternative.
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 building a machine learning model to predict customer churn. The data engineering team has extracted customer transaction data from Amazon Aurora and stored it as CSV files in Amazon S3. The data includes customer IDs, transaction amounts, timestamps, and product categories. A data scientist discovers that the dataset contains several missing values in the 'transaction_amount' column for about 15% of the records. The data scientist also notices that the 'customer_id' column has some duplicate entries. The team wants to prepare the data for training a churn model using Amazon SageMaker. The data is approximately 50 GB in size. What should the data scientist do to handle the missing values and duplicates efficiently while preparing the data for training?
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 an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.
Option B is correct because AWS Glue ETL jobs are serverless and designed to handle large-scale data transformations (like 50 GB) without requiring manual cluster management. Glue can read CSV files from S3, apply transformations to impute missing values with the mean or median, drop duplicate customer IDs, and write the cleaned data back to S3, all while scaling automatically to handle the data volume efficiently.
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 a SageMaker notebook instance with Pandas to load the entire dataset into memory, fill missing values with the median, and drop duplicate customer IDs.
Why it's wrong here
Loading 50 GB into a notebook instance's memory is inefficient and may cause out-of-memory errors; Pandas is not distributed.
- ✓
Use an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.
Why this is correct
Glue is serverless, scales automatically, and is suitable for 50 GB. It can efficiently handle missing value imputation and deduplication.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Drop all records with missing values in the transaction_amount column and remove duplicate customer IDs using an Athena SQL query, then store the result in S3.
Why it's wrong here
Dropping all records with missing values would discard 15% of data, leading to potential bias and loss of information.
- ✗
Use an Amazon EMR cluster with Spark to read the CSV files, impute missing transaction amounts with the mean or median, and remove duplicate customers.
Why it's wrong here
EMR is unnecessarily complex for a simple fill and dedup operation; AWS Glue is a simpler and cost-effective alternative.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Option A (Pandas in a notebook) because it seems simple, but they overlook the memory limitations of a single-instance notebook when processing 50 GB of data, which is a classic 'scale vs. simplicity' trick in the MLA-C01 exam.
Detailed technical explanation
How to think about this question
AWS Glue ETL jobs use Apache Spark under the hood, enabling distributed processing of large datasets like 50 GB across multiple workers. Glue's DynamicFrame API automatically handles schema inference and can apply transformations such as FillMissingValues (which imputes with mean, median, or mode) and DropDuplicates. In a real-world scenario, using Glue's built-in transform 'DropDuplicates' on the 'customer_id' column ensures that only the first occurrence is kept, which is critical for churn prediction where each customer should appear only once.
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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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 an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3. — Option B is correct because AWS Glue ETL jobs are serverless and designed to handle large-scale data transformations (like 50 GB) without requiring manual cluster management. Glue can read CSV files from S3, apply transformations to impute missing values with the mean or median, drop duplicate customer IDs, and write the cleaned data back to S3, all while scaling automatically to handle the data volume efficiently.
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.
About these practice questions
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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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