The answer is to use only past data for feature engineering, standardize using parameters from the training set only, and avoid any lookahead in window-based aggregations. These three steps are critical for preventing data leakage in time series preprocessing because temporal order must be strictly preserved; if you compute the mean and standard deviation from the entire dataset before splitting, the test set’s distribution leaks into training, allowing the model to see future information and inflate performance metrics. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of temporal validation versus random splits—a common trap is applying global scaling or creating lag features that accidentally include future values. Remember the memory tip: “Train on the past, scale on the train, and never peek ahead.”
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.
You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Standardize features using parameters computed only from the training set
Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.
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.
✗
Impute missing values using the mean of the entire dataset
Why it's wrong here
Using the full dataset mean leaks information from test data.
✓
Standardize features using parameters computed only from the training set
Why this is correct
Computing mean and variance only on training data prevents leakage from test.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a time-based train/test split
Why this is correct
Ensures training data is chronologically before test data.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use only past data for feature engineering (e.g., lag features)
Why this is correct
Lag features based on past observations do not leak future information.
Related concept
Read the scenario before looking for a memorised answer.
✗
Shuffle the data randomly before splitting
Why it's wrong here
Shuffling ignores time order, causing future data to influence training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that standard preprocessing techniques like imputation or scaling can be applied globally to the entire dataset, when in time-series contexts they must be computed only from the training set to avoid leakage.
Detailed technical explanation
How to think about this question
In time-series forecasting, data leakage often occurs when preprocessing steps like scaling or imputation use global statistics. For example, using `StandardScaler` from scikit-learn on the full dataset before splitting computes the mean and variance from all time steps, including future ones. A robust approach is to fit the scaler only on the training set and then transform the test set using those fitted parameters, ensuring the model never sees future distributional information.
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: Standardize features using parameters computed only from the training set — Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?
easy
A.Stratified split based on target
✓ B.Temporal split based on date
C.Random split with 70/20/10
D.K-fold cross-validation
Why B: Option B is correct because a temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.
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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.
Question Discussion
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