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CertNexus AIP-210 Exam - Topic 3 Question 62 Discussion

You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
C) Ridge regression
A) Deep learning neural network
B) Random forest
D) Support-vector machine

CertNexus AIP-210 Exam - Topic 3 Question 62 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 62
Topic #: 3
[All AIP-210 Questions]

You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?

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Suggested Answer: C

Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.


by Aileen at Aug 12, 2026, 02:48 PM

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Golda
3 days ago
Ridge regression is usually lightweight.
upvoted 0 times
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Howard
9 days ago
Support-vector machines can be efficient, but I recall they might struggle with larger datasets. I wonder if ridge regression is still the best fit.
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Lezlie
14 days ago
Random forests can be quite powerful, but they can also be memory-heavy. I think I need to double-check their resource requirements.
upvoted 0 times
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Johnetta
19 days ago
I feel like ridge regression might be a good option since it's simpler and less resource-intensive, but I'm not entirely sure.
upvoted 0 times
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Adell
24 days ago
I remember discussing that deep learning models usually require a lot of resources, so I don't think that's the right choice here.
upvoted 0 times
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