Most Recent CertNexus AIP-210 Exam Questions & Answers

Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.

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.

Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. Reference: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating

A type 1 error in statistical hypothesis testing is when the null hypothesis is true, but is rejected. This means that the test falsely concludes that there is a significant difference or effect when there is none. The probability of making a type 1 error is denoted by alpha, which is also known as the significance level of the test. A type 1 error can be reduced by choosing a smaller alpha value, but this may increase the chance of making a type 2 error, which is when the null hypothesis is false but fails to be rejected. Reference: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]