MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Split the data by time, using data before a certain date for training and after for validation
Option D is correct because splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.
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 stratified sampling based on churn label
Why it's wrong here
Stratified sampling does not consider time order.
✗
Randomly split the data 80/20 for training and validation
Why it's wrong here
Random split may cause temporal leakage if not time-aware.
✗
Use k-fold cross-validation with shuffling
Why it's wrong here
Shuffling can mix future data into training folds.
✓
Split the data by time, using data before a certain date for training and after for validation
Why this is correct
Time-based split ensures no future data influences training.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the concept of data leakage in time-series contexts, where candidates mistakenly choose random splits or cross-validation with shuffling, overlooking that temporal order must be preserved to avoid future data leaking into training.
Detailed technical explanation
How to think about this question
In time-series forecasting, a common technique is to use a time-based split (e.g., cutoff date) or walk-forward validation (expanding window or sliding window) to maintain temporal order. For churn prediction, customer behavior evolves over time; using future data to predict past churn would create a model that fails in production. The split should be based on a specific timestamp column, ensuring no training sample has a timestamp later than any validation sample.
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.
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: Split the data by time, using data before a certain date for training and after for validation — Option D is correct because splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.
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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Question Discussion
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