Q: 15
[Machine Learning Implementation and Operations]
A Machine Learning Specialist deployed a model that provides product recommendations on a
company's website Initially, the model was performing very well and resulted in customers buying
more products on average However within the past few months the Specialist has noticed that the
effect of product recommendations has diminished and customers are starting to return to their
original habits of spending less The Specialist is unsure of what happened, as the model has not
changed from its initial deployment over a year ago
Which method should the Specialist try to improve model performance?
Options
Discussion
Option D is the way to go. Retraining with original data plus new data is key since user behavior and inventory shift over time. B is a trap here, just tuning hyperparams won't fix stale training data. Seen similar on other practice sets.
D imo. Model drift is likely, so adding new data as inventory changes makes sense.
D feels right since product recommendations change as inventory and user patterns shift. I remember seeing tips in the official guide suggesting periodic retraining with recent data. Anyone else using practice exams see it framed like this?
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