AI0-001 AI Models and Data Engineering Practice Question
A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days. The data engineering team has built a pipeline that collects data from multiple sources, including electronic health records (EHR), lab results, and wearable device data. During initial testing, the model's performance is poor, with high false positives. Upon investigation, the team discovers that the data contains significant temporal misalignment: lab results are timestamped when ordered, not when collected; wearable data is aggregated hourly; and EHR data has inconsistent update frequencies. The data pipeline currently joins all features on the patient ID without aligning timestamps. The data volume is large, and processing time is a concern. Which action should the data engineering team take to most effectively address the issue and improve model performance?
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
✓
Implement a window-based feature aggregation (e.g., 6-hour windows) and align all features to the same time windows before joining.
Implementing a window-based feature aggregation with consistent time windows (e.g., 6-hour or 12-hour) and aligning all data to those windows before joining ensures temporal consistency and reduces noise. This approach addresses the root cause of misalignment while managing data volume through aggregation. Simply discarding data or padding with zeros loses valuable information. Using an interpolation algorithm may introduce unrealistic values for irregularly sampled data. Leaving the pipeline as-is and tuning the model does not fix the data quality issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Discard all records where timestamps do not match exactly across sources, and only use records with perfect alignment.
Why it's wrong here
This would discard most of the data, reducing sample size and likely causing bias.
- ✓
Implement a window-based feature aggregation (e.g., 6-hour windows) and align all features to the same time windows before joining.
Why this is correct
This creates consistent timestamps and reduces noise through aggregation, effectively addressing misalignment.
- ✗
Leave the pipeline unchanged and instead adjust the model's classification threshold to reduce false positives.
Why it's wrong here
Tuning the threshold does not fix the underlying data quality issue; model performance will remain suboptimal.
- ✗
Use a data imputation algorithm to fill in missing timestamps and then join on the nearest timestamp.
Why it's wrong here
Imputation may introduce unrealistic values for timestamps, and nearest-neighbor join could still cause misalignment.
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Written by Johnson Ajibi, MSc IT Security
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