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CertNexus AIP-210 Exam - Topic 2 Question 41 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 41
Topic #: 2
[All AIP-210 Questions]

Which two of the following criteria are essential for machine learning models to achieve before deployment? (Select two.)

Show Suggested Answer Hide Answer
Suggested Answer: C, E

Scalability and explainability are two criteria that are essential for ML models to achieve before deployment. Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions. Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.


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My
2 months ago
Explainability seems overrated to me.
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Gladis
3 months ago
I think scalability is super important too.
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Angella
3 months ago
Definitely need data size and explainability!
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Cherrie
3 months ago
Wait, can a model really be effective without portability?
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Lucille
3 months ago
Complexity? Not sure that’s essential.
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Noel
3 months ago
Complexity seems like it could be a factor, but I remember discussing how it might not be essential for all models. I guess I'm leaning towards explainability and scalability.
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Wendell
4 months ago
I feel like scalability is important too, especially if the model needs to handle more data later on. But I can't recall if it was one of the top two criteria.
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Glenn
4 months ago
I'm not so sure about data size being essential. I remember a practice question where it mentioned that sometimes smaller datasets can still be effective.
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Jose
4 months ago
I think explainability is definitely one of the key criteria. We need to understand how the model makes decisions, right?
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Elenor
4 months ago
This is a tricky one, but I'm pretty confident I know the answer. Explainability and portability are the two essential criteria for model deployment.
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Colette
5 months ago
I think the key here is to focus on the model's performance and reliability. Explainability and data size are probably the most important factors to consider.
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Frederica
5 months ago
Okay, I've got this. Explainability and scalability are definitely essential for deploying a machine learning model. The other options are less critical.
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Graciela
5 months ago
Hmm, I'm a bit unsure about this one. Explainability and portability seem important, but I'm not sure about the other criteria. I'll have to think it through.
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Jose
5 months ago
This question seems straightforward, but I want to make sure I understand the key criteria for model deployment. I'll review the options carefully.
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Nohemi
10 months ago
Ha, this is easy! Clearly, the answer is C and E. Explainability and scalability are the yin and yang of machine learning. Anything else is just window dressing.
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Joye
8 months ago
Complexity and data size are important, but explainability and scalability are key for deployment.
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Micaela
9 months ago
I agree, those two criteria are definitely essential for machine learning models.
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Jaclyn
9 months ago
I think you're right, explainability and scalability are crucial for deployment.
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Daron
10 months ago
Wow, this is a tough one. I'm going with C and E - explainability and scalability are like the peanut butter and jelly of ML models. Gotta have 'em both!
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Elena
10 months ago
Hold up, I think B and E are the real winners here. Data size and scalability are the foundation for any ML model worth its salt. Explainability? That's for the nerds.
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Starr
9 months ago
Portability could also be important for deploying ML models in different environments.
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Malcom
9 months ago
Explainability might not be as important as the other factors.
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Tamra
9 months ago
Complexity and portability are important too, but B and E are top priorities.
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Stefanie
9 months ago
Explainability might be important for some cases, but B and E are definitely key.
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Deane
9 months ago
I agree, data size and scalability are crucial for deployment.
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Evangelina
10 months ago
I agree, data size and scalability are crucial for ML models to succeed.
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Lizette
10 months ago
I think E) Scalability is crucial too. It's important for the model to handle large amounts of data.
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Avery
11 months ago
Hmm, I'd say C and D are the way to go. Portability is key for real-world deployment, and explainability is a must for regulatory compliance.
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Cherry
10 months ago
Explainability is also important for ensuring transparency and trust in the model's predictions.
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Tamekia
10 months ago
I agree, portability is crucial for deploying models across different environments.
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Trevor
11 months ago
C and E are definitely the most important. Who cares about explainability when you can just throw more data at it, right?
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Jeniffer
11 months ago
I agree with you, Sylvia. I also believe D) Portability is essential for deployment.
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Sylvia
11 months ago
I think C) Explainability is important for machine learning models.
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