Reliable C1000-177 Dumps (V8.02) – Helping You Practice the Verified C1000-177 Questions for the Exam Success

1. Why is it important to create hypotheses about the behavior of the AI system?

2. In the context of machine learning, what does the term 'model drift' refer to?

3. Which approach is recommended for prioritizing business opportunities when planning an MVP?

4. What is the primary purpose of monitoring a model in production?

5. Which of the following are essential tasks when preparing data for exploratory analysis? (Choose Three)

6. In the context of classification, what does the term 'overfitting' refer to?

7. Which of the following are considered direct effects of an AI solution? (Choose Two)

8. How do you assess the feasibility of an AI solution?

9. What is the primary use of the WHERE clause in an SQL query?

10. What is the first step in aligning on user intents for an AI solution?

11. How does IBM Garage Methodology suggest measuring success for an MVP?

12. In assessing progress on the AI Ladder, which aspects should be considered? (Choose Two)

13. When monitoring models in production, what aspect is crucial for maintaining long-term reliability?

14. Which feature engineering technique can be used to simplify models and improve interpretability?

15. How does feature scaling benefit the process of exploratory data analysis?

16. For implementing dimensional reduction, which method would be most effective when dealing with highly nonlinear data?

17. What are two reasons a data point would be treated as an outlier?

18. Which practice is least effective in configuring environments for training machine learning models?

19. Why is logistic regression considered a linear classifier?

20. What considerations should be made when evaluating the ethical implications of a business problem? (Choose Three)