MLS-C01 Modeling Practice Question
A company wants to build a real-time anomaly detection system for IoT sensor data. The data arrives as a stream of numerical values. The model should adapt to concept drift over time. Which approach is most suitable?
⚠ Common exam trap
AWS often tests the misconception that stateful recurrent models (like LSTMs) inherently adapt to concept drift, but without weight updates they remain static; the trap here is confusing 'statefulness' (which preserves temporal context across batches) with 'online learning' (which updates model parameters).
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
✓
Train an online learning model, such as stochastic gradient descent (SGD) with a sliding window
Online learning with stochastic gradient descent (SGD) using a sliding window allows the model to continuously update its parameters as new IoT sensor data arrives, adapting to concept drift without retraining from scratch. The sliding window ensures that the model focuses on the most recent data distribution, discarding outdated patterns, which is essential for real-time anomaly detection in streaming environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Train an online learning model, such as stochastic gradient descent (SGD) with a sliding window
Why this is correct
Online learning updates the model incrementally, allowing adaptation to concept drift.
- ✗
Use a static deep learning model trained once on historical data
Why it's wrong here
Static models cannot adapt to drift.
- ✗
Use a stateful LSTM with fixed weights
Why it's wrong here
Fixed weights mean no adaptation.
- ✗
Batch train a random forest model monthly
Why it's wrong here
Monthly retraining is too infrequent for real-time drift adaptation.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.