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2025 AIP-210: CertNexus Certified Artificial Intelligence Practitioner (CAIP) Updated Free Practice

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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

Understanding the Artificial Intelligence Problem
Analyze the use cases of ML algorithms to rank them by their success probability

Topic 2

Design machine and deep learning models
Explain data collection
transformation process in ML workflow

Topic 3

Identify potential ethical concerns
Analyze machine learning system use cases

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q84-Q89):

NEW QUESTION # 84
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?

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

Answer: D

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 # 85
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)

A. Explainability
B. Complexity
C. Data size
D. Scalability
E. Portability

Answer: A,D

Explanation:
Explanation
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.

NEW QUESTION # 86
Which of the following can take a question in natural language and return a precise answer to the question?

A. Spark ML
B. Databricks
C. IBM Watson
D. Pandas

Answer: C

Explanation:
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.

NEW QUESTION # 87
Normalization is the transformation of features:

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

Answer: D

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 # 88
Which of the following approaches is best if a limited portion of your training data is labeled?

A. Dimensionality reduction
B. Reinforcement learning
C. Semi-supervised learning
D. Probabilistic clustering

Answer: C

Explanation:
Explanation
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled.
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.

NEW QUESTION # 89
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