The answer is to resample the data to a fixed frequency. This preprocessing step is essential because rolling window statistics, such as moving averages or standard deviations, rely on a consistent time index to define uniform window boundaries. Without resampling, irregular timestamps caused by network issues create gaps or misaligned intervals, making it impossible to compute accurate aggregations over fixed windows—the resulting features would be biased or incomplete, undermining the anomaly detection model. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of time series resampling as a prerequisite for feature engineering, often appearing in scenario-based questions about sensor or IoT data. A common trap is jumping straight to imputation or interpolation without first establishing a uniform timestamp grid; remember that resampling must come before any rolling calculation. Memory tip: “Resample first, roll second—gaps wreck the window.”
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 company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Resample data to a fixed frequency
Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.
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.
✗
Remove missing timestamps
Why it's wrong here
Removing timestamps can break continuity but does not create regular intervals.
✓
Resample data to a fixed frequency
Why this is correct
Resampling ensures consistent time intervals, which is required for rolling windows.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sort data by device ID
Why it's wrong here
Sorting is important but does not address the irregular time intervals.
✗
Impute missing values with forward fill
Why it's wrong here
Imputation fills gaps but does not create regular timestamps; resampling is needed first.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between handling missing values (imputation) and handling irregular timestamps (resampling), leading candidates to confuse forward-fill as a solution for time alignment when it only addresses missing data points, not the underlying time index irregularity.
Detailed technical explanation
How to think about this question
Resampling to a fixed frequency (e.g., 1-minute intervals) using pandas `resample()` or similar tools creates a regular time series where each window contains the same number of observations. This is critical for rolling statistics like moving averages or standard deviations, which assume equal spacing. In real-world IoT pipelines, sensor data often arrives with jitter or gaps, and resampling with aggregation (e.g., mean, count) ensures the feature matrix has a consistent temporal structure for downstream ML models.
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: Resample data to a fixed frequency — Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.
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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