MLA-C01 Data Preparation for Machine Learning Practice Question
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?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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.
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.
- ✗
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
An Amazon EMR cluster with Spark is over-provisioned for 50 GB of CSV data, as SageMaker's built-in Data Wrangler or a processing job can handle imputation and deduplication directly without spinning up a separate cluster, adding orchestration overhead. This option is tempting because Spark is a standard tool for large-scale data transformation, and it would be correct if the dataset exceeded hundreds of gigabytes or required complex distributed joins across multiple sources.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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