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AIP-210 PDF

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  • Questions: 90 Q&A's With Detailed Explanation
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  • Exam Name: CertNexus Certified Artificial Intelligence Practitioner (CAIP)
  • Last Update: 04-Oct-2024
  • Questions and Answers: 90
  • Single Choice: 78 Q&A's
  • Multiple Choice: 12 Q&A's

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AIP-210 Questions and Answers

Question # 1

Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?

Definition of logit-transformation

If p is the proportion: logit(p)=log(p/(l-p))

A.

After logit-transformation, the data may violate the assumption of independence.

B.

Noisy data could become more influential in your model.

C.

The model will be more likely to violate the assumption of normality.

D.

Values near 0.5 before logit-transformation will be near 0 after.

Question # 2

Which of the following describes a typical use case of video tracking?

A.

Augmented dreaming

B.

Medical diagnosis

C.

Traffic monitoring

D.

Video composition

Question # 3

A data scientist is tasked to extract business intelligence from primary data captured from the public. Which of the following is the most important aspect that the scientist cannot forget to include?

A.

Cyberprotection

B.

Cybersecurity

C.

Data privacy

D.

Data security

Question # 4

Which two encodes can be used to transform categories data into numerical features? (Select two.)

A.

Count Encoder

B.

Log Encoder

C.

Mean Encoder

D.

Median Encoder

E.

One-Hot Encoder

Question # 5

You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?

A.

When it is necessary to save computational time.

B.

When the categories of the dependent variable are not linearly separable.

C.

When the distribution of the dependent variable is Gaussian.

D.

When there is high correlation among the features.

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