Question 9
Content Domain 3: ModelingA machine learning team needs a training approach that can update the model continuously as new data arrives rather than waiting to retrain on a fixed dataset. Which approach best fits this requirement?
Correct answer: C
Explanation
Online or real-time learning updates a model as new data arrives, while batch learning trains on a fixed dataset at discrete intervals. — Batch or real-time/online — Key Terms: batch, real-time, online
Why each option is right or wrong
A. Batch training on a fixed dataset at scheduled intervals
Batch learning uses fixed datasets and discrete retraining cycles rather than continuous updates.
B. Offline training that delays model changes until a later run
Delayed retraining does not update the model as each new data point arrives.
C. Online learning that updates the model as new data arrives
The requirement is continuous model updating as new data arrives. Under the batch versus real-time/online distinction in the source material, online learning is the approach aligned with immediate or ongoing updates rather than waiting for a separate fixed-dataset retraining cycle.
D. Periodic retraining after collecting a full new data batch
Periodic retraining waits for accumulated data instead of updating continuously on arrival.