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Question 62 - DSA-C02 discussion

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You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.

What should you do?

A.
Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.
Answers
A.
Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.
B.
Create a new version of the dataset using only the new data and retrain the model.
Answers
B.
Create a new version of the dataset using only the new data and retrain the model.
C.
Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
Answers
C.
Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
D.
Retrained your training dataset after correcting data outliers & no need to introduce new data.
Answers
D.
Retrained your training dataset after correcting data outliers & no need to introduce new data.
Suggested answer: A

Explanation:

To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

These are important metrics to track once models are deployed to production. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

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asked 23/09/2024
Adriano Costa
36 questions
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