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CertNexus AIP-210 Exam Syllabus Topics:

Topic Details Topic 1 Transform numerical and categorical data
Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 2 Train, validate, and test data subsets
Training and Tuning ML Systems and Models
Topic 3 Identify potential ethical concerns
Analyze machine learning system use cases
Topic 4 Address business risks, ethical concerns, and related concepts in training and tuning
Work with textual, numerical, audio, or video data formats

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q69-Q74):

NEW QUESTION # 69

Normalization is the transformation of features:

A. By subtracting from the mean and dividing by the standard deviation.
B. So that they are on a similar scale.
C. Into the normal distribution.
D. To different scales from each other.

Answer: B

Explanation:

Explanation

Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or

-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k-nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization - Quantitative analysis]

NEW QUESTION # 70

An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.

Which ethical principle is this an example of?

A. Privacy
B. Fairness
C. Transparency
D. Safety

Answer: C

Explanation:

Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system's actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system's recommendations, as well as facilitate auditing and feedback.

NEW QUESTION # 71

Which of the following pieces of AI technology provides the ability to create fake videos?

A. Long short-term memory (LSTM) networks
B. Recurrent neural networks (RNN)
C. Support-vector machines (SVM)
D. Generative adversarial networks (GAN)

Answer: D

Explanation:

Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.

NEW QUESTION # 72

Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?

A. Ridge regression
B. Clustering
C. Polynomial regression
D. Linear regression

Answer: A

Explanation:

Explanation

Ridge regression is a type of regularization technique that can help reduce collinearity among independent variables. It does this by adding a penalty term to the ordinary least squares (OLS) objective function, which shrinks the coefficients of highly correlated variables towards zero. This reduces the variance of the coefficient estimates and improves the stability and accuracy of the regression model. References: Multicollinearity in Regression Analysis: Problems, Detection, and Solutions - Statistics By Jim, A Beginner's Guide to Collinearity: What it is and How it affects our regression model - StrataScratch

NEW QUESTION # 73

Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?

A. Algorithmic bias
B. Systematic value distortion
C. Stereotype bias
D. Sampling bias

Answer: D

Explanation:

Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]

NEW QUESTION # 74

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CertNexus AIP-210 Exam Syllabus Topics:

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q69-Q74):

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