CertNexus AIP-210 Exam - Topic 2 Question 7 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 7
Topic #: 2

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?

APeriodically before New Year's Day and after New Year's Day

BPeriodically every year

CWhen concept drift is detected

DWhen data drift is detected

Show Suggested Answer

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.

by Arlette at Oct 20, 2023, 09:38 PM

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Marsha

10 months ago

D could be important too, especially with changing trends!

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Jill

10 months ago

A is a good idea, but it might not be enough.

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Paola

10 months ago

Wait, can you really detect concept drift? Sounds complicated!

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Keena

11 months ago

I disagree, B seems more consistent for yearly resolutions.

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Odette

11 months ago

I think option C makes the most sense for retraining.

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Tamra

11 months ago

I lean towards option B since it suggests a regular schedule, but I wonder if it’s enough without monitoring for changes.

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Theresia

11 months ago

I practiced a similar question where timing was key. I feel like option A might be too rigid for a dynamic system like this.

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Xochitl

11 months ago

I'm not sure, but I remember something about data drift being important too. Maybe option D could be relevant?

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Horace

12 months ago

I think option C about concept drift makes sense because people's resolutions can change over time, right?

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Glendora

12 months ago

Option B, retraining periodically every year, seems like a good baseline approach. That way we can stay on top of changes without relying on potentially unreliable drift detection.

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Heike

12 months ago

I'm leaning towards option C - retraining when concept drift is detected. Since the system is recommending New Year's resolutions, the underlying data and patterns could shift significantly over time, so we'd want to be responsive to that.

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Marcos

12 months ago

Hmm, I'm a bit confused. Does the lack of monitoring components mean we can't detect concept or data drift? That could impact the best retraining strategy.

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Phuong

12 months ago

This seems like a tricky one. I'll need to think carefully about the different retraining strategies and how they might apply to this specific scenario.

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Virgie

12 months ago

Okay, I think I've got this. Since R-LFA and TI-LFA are both enabled, R1 will use an R-LFA path that doesn't coincide with the post-convergence path. Option B looks like the right answer.

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Yan

12 months ago

I think the REST API is the way to go here. It's the most modern and flexible option for accessing the Attribute Group schema.

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Jennifer

12 months ago

I've got this! The IP address and netmask of the host via the management port are the two parameters that can be set during the initial BIG-IP system configuration. Easy peasy.

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Trinidad

1 year 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

1 year ago

B) Periodically every year

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Irving

1 year ago

C) When concept drift is detected

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Eleonora

1 year ago

A) Periodically before New Year's Day and after New Year's Day

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Quentin

1 year 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

1 year ago

Kayleigh: Agreed, we need to make sure it's giving out helpful recommendations.

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Kayleigh

1 year ago

User 2: Definitely! We should retrain the AI system when concept drift is detected.

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Hershel

1 year ago

User 1: Haha, that would be funny! 'Eat more ice cream' as a resolution.

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Carey

1 year 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 year 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

1 year 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

1 year ago

C) When concept drift is detected

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Georgeanna

1 year ago

B) Periodically every year

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Roosevelt

1 year ago

A) Periodically before New Year's Day and after New Year's Day

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Carmen

1 year 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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