Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 1 discussion

Exam question from Amazon's AWS Certified AI Practitioner AIF-C01

Question #: 1
Topic #: 1

[All AWS Certified AI Practitioner AIF-C01 Questions]

A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?

A. Code for model training
B. Partial dependence plots (PDPs)
C. Sample data for training
D. Model convergence tables

Show Suggested Answer

Suggested Answer: B 🗳️

by p2pcerts at Nov. 3, 2024, 8:52 a.m.

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p2pcerts

Highly Voted 2 months, 1 week ago

B. Partial dependence plots (PDPs)

upvoted 5 times

Seraphina1

1 month, 1 week ago

Nice take on PDPs! p2pcerts looks like a great resource too!

upvoted 1 times

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leyamo

Most Recent 5 days, 20 hours ago

Selected Answer: B

B. Partial Dependence Plots (PDPs) Explanation: Partial Dependence Plots (PDPs) are useful tools for understanding the relationship between specific features and the model's predictions, making it easier to see how changes in input variables affect the forecast. Thanks to Examforsure Their MLA-C01 material was the key to my exam success. PDPs are particularly helpful for stakeholders because they visually show the impact of individual features on predictions without requiring a deep understanding of the model's inner workings.

upvoted 2 times

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jove

2 months ago

Selected Answer: B

B. Partial Dependence Plots (PDPs) Explanation: Partial Dependence Plots (PDPs) are useful tools for understanding the relationship between specific features and the model's predictions, making it easier to see how changes in input variables affect the forecast. PDPs are particularly helpful for stakeholders because they visually show the impact of individual features on predictions without requiring a deep understanding of the model's inner workings.

upvoted 4 times

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sacha12

2 months, 1 week ago

I think B is correct