Pass the CertNexus Certified AI Practitioner AIP-210 Questions and answers with CertsForce

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.

To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:

minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1)

Plugging in the given values, we get:

minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)

minimum grade = (560 - 469) / 1

minimum grade = 91

Therefore, the student needs to score at least 91 on the last test to get an 80% average.

Overfitting is a problem that occurs when a model learns too much from the training data and fails to generalize well to new or unseen data. Overfitting can result from using a low quantity of training data, a high complexity of the model, or a lack of regularization. Overfitting can also increase the risk of reverse-engineering a dataset from a model’s outputs, as the model may reveal too much information about the specific features or patterns of the training data. This can break the confidentiality of the data and expose sensitive information about the individuals in the dataset .

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.

This SQL query provides the Directors’ Firstname, Lastname, the name of their departments, and the average employee’s salary by joining the three tables using the appropriate join types and conditions. The RIGHT JOIN between Employee_Table and Department_Table ensures that all departments are included in the result, even if they have no employees. The INNER JOIN between Department_Table and Directorjable ensures that only departments with directors are included in the result. The GROUP BY clause groups the result by the directors’ names and departments’ names, and calculates the average salary for each group using the AVG function. References: SQL Joins - W3Schools, SQL GROUP BY Statement - W3Schools

An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:

Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.

Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.

ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.

Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.

Harms modeling is a technique that helps product managers design AI solutions responsibly by evaluating both positive and negative outcomes. Harms modeling involves creating a shared taxonomy of potential negative impacts and conducting an assessment along vectors such as severity, impact, frequency, and likelihood. Harms modeling can help identify and mitigate any risks or harms that may arise from using AI solutions. References: [Harms Modeling for Responsible AI | by Google Developers | Google Developers], [Harms Modeling for Responsible AI - YouTube]