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 data scientist is preprocessing time series data for a fraud detection model. The data includes transaction timestamps, amounts, and merchant IDs. The model should predict fraud within seconds of a transaction. The data scientist wants to avoid data leakage by not using future information to predict past events. Which data preparation practice should be implemented?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Compute features like lagged transaction amounts and rolling statistics based only on each transaction's past data up to that point.
Option A is correct because it ensures that features are computed using only historical data available up to each transaction's timestamp, preventing any future information from leaking into the model. In time series fraud detection, using only past data for lagged amounts and rolling statistics respects the temporal order and avoids the model learning patterns that would not be available at prediction time.
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.
✓
Compute features like lagged transaction amounts and rolling statistics based only on each transaction's past data up to that point.
Why this is correct
This ensures no future information is used.
Related concept
Read the scenario before looking for a memorised answer.
✗
Randomly shuffle the dataset before splitting into training and validation sets.
Why it's wrong here
Shuffling temporal data breaks the time dependency and causes leakage.
✗
Generate features such as rolling averages and lag features using a sliding window of all available data.
Why it's wrong here
Using all data including future to compute rolling windows causes leakage.
✗
Normalize the features using MinMaxScaler on the entire dataset before splitting into training and testing.
Why it's wrong here
Fitting scaler on the entire dataset leaks information about future data ranges.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the concept of temporal data leakage by presenting options that seem statistically sound (like shuffling or global normalization) but violate the time series assumption, leading candidates to overlook the need for chronological feature engineering.
Detailed technical explanation
How to think about this question
In time series preprocessing, the key is to compute features in a strictly chronological manner, often using expanding windows or rolling windows that only look backward. For fraud detection, this means each transaction's features (e.g., average transaction amount over the last hour) must be calculated from transactions that occurred before it, not including the current or future transactions. Real-world implementations often use tools like pandas' `shift()` and `rolling()` with closed='left' to ensure no lookahead bias.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Compute features like lagged transaction amounts and rolling statistics based only on each transaction's past data up to that point. — Option A is correct because it ensures that features are computed using only historical data available up to each transaction's timestamp, preventing any future information from leaking into the model. In time series fraud detection, using only past data for lagged amounts and rolling statistics respects the temporal order and avoids the model learning patterns that would not be available at prediction time.
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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