AI0-001 AI Implementation and Operations Practice Question
A data engineering team is designing a pipeline to train a model on streaming data. The data arrives in a time-series format. Which approach should they use to ensure the model reflects current trends without catastrophic forgetting?
⚠ Common exam trap
CompTIA often tests the misconception that a sliding window of recent data alone prevents catastrophic forgetting, but without a mechanism like elastic weight consolidation or replay buffers, the model still forgets older but recurring patterns.
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 incremental learning with periodic validation
Incremental learning (also called online learning) allows the model to update its parameters continuously as new streaming data arrives, without requiring access to historical data. By coupling this with periodic validation on a held-out set, the team can detect concept drift and ensure the model adapts to current trends while avoiding catastrophic forgetting, which occurs when new updates overwrite previously learned patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement incremental learning with periodic validation
Why this is correct
Incremental learning adapts to new data while retaining previous knowledge.
- ✗
Use a sliding window of the most recent data for training
Why it's wrong here
Sliding window may cause catastrophic forgetting of long-term patterns.
- ✗
Deploy an ensemble of models trained on different time periods
Why it's wrong here
Ensemble does not inherently prevent forgetting.
- ✗
Retrain the entire model from scratch every week
Why it's wrong here
Full retraining is resource-intensive and may not capture rapid shifts.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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