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CertNexus Exam AIP-210 Topic 2 Question 7 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 7
Topic #: 2
[All AIP-210 Questions]

An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components. What retraining strategy would be BEST for this pipeline?

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Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.


Contribute your Thoughts:

Trinidad
28 days ago
I'm just hoping the AI doesn't start suggesting resolutions like 'become a professional couch potato' or 'learn to speak fluent gibberish'.
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Tiara
13 days ago
B) Periodically every year
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Irving
16 days ago
C) When concept drift is detected
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Eleonora
21 days ago
A) Periodically before New Year's Day and after New Year's Day
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Quentin
1 months ago
Haha, imagine an AI system recommending resolutions like 'eat more ice cream' or 'nap for 12 hours a day'. Definitely need to keep a close eye on that concept drift!
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Sharen
15 days ago
Kayleigh: Agreed, we need to make sure it's giving out helpful recommendations.
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Kayleigh
19 days ago
User 2: Definitely! We should retrain the AI system when concept drift is detected.
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Hershel
23 days ago
User 1: Haha, that would be funny! 'Eat more ice cream' as a resolution.
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Carey
1 months ago
D) When data drift is detected could also work, but concept drift is more important for this use case. The system needs to understand the changing meanings of 'New Year's resolutions' over time.
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Tammi
1 months ago
I agree, C is the way to go. Retraining based on concept drift will ensure the system stays aligned with the user's evolving needs.
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Pete
2 days ago
I agree, C is the way to go. Retraining based on concept drift will ensure the system stays aligned with the user's evolving needs.
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Benton
24 days ago
C) When concept drift is detected
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Georgeanna
26 days ago
B) Periodically every year
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Roosevelt
28 days ago
A) Periodically before New Year's Day and after New Year's Day
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Carmen
2 months ago
C) When concept drift is detected seems like the best option. Monitoring for concept drift is crucial for an AI system that makes recommendations, as the user's preferences and the relevance of the resolutions can change over time.
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Lyda
25 days ago
User 2: I agree, the AI system needs to adapt to changes in user preferences.
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Lyndia
29 days ago
User 1: I think retraining when concept drift is detected is important for accurate recommendations.
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Mitzie
2 months ago
But wouldn't it also be helpful to retrain periodically every year to ensure the recommendations stay relevant?
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Candra
2 months ago
I agree with Josephine. It's important to update the AI system's recommendations when there are changes in the data distribution.
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Josephine
2 months ago
I think the best retraining strategy would be when concept drift is detected.
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