Practice Step 7: Monitoring And Feedback Loop (18.3.7) - Data Science for Business and Decision- Making

Practice: Step 7: Monitoring and Feedback Loop

Lesson 18.3.7Data Science for Business and Decision- Making

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is model drift?
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Think about how predictions can vary as conditions change.
Question 2.
Why is retraining important?
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Consider accuracy in business decisions.
3Question 3.
What is model drift?
- Improvement in model accuracy
- Decline in model performance
- Consistent model results
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Think about how models perform over time.
4Question 4.
True or False: Periodic retraining of models is unnecessary if they performed well initially.
- True
- False
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Consider the concept of changing data.
Question 5.
You are managing an AI recommendation system for an e-commerce site. Describe the steps you'd implement to ensure the system remains effective over time.
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Consider how you would approach ongoing evaluation.
Question 6.
A financial institution's forecasting model has not been retrained for two years. Analyze potential impacts of this stagnation.
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Think about how financial markets fluctuate.

5 more questions in this section. Enroll free to practise them all.