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CertNexus AIP-210 Exam Questions
- Topic 1: Identify potential ethical concerns/ Analyze machine learning system use cases
- Topic 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
- Topic 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
- Topic 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
- Topic 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
- Topic 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
- Topic 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow
Free CertNexus AIP-210 Exam Actual Questions
Note: Premium Questions for AIP-210 were last updated On Apr. 11, 2025 (see below)
Which two of the following criteria are essential for machine learning models to achieve before deployment? (Select two.)
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.
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?
Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?
The optimal value of K is the one that minimizes the inertia or within-cluster sum of squares, while avoiding too many clusters that may overfit the data. The elbow plot shows a sharp decrease in inertia from K = 1 to K = 2, and then a more gradual decrease from K = 2 to K = 3. After K = 3, the inertia does not change much as K increases. Therefore, the elbow point is at K = 3, which is the optimal value of K for this data. Reference: How to Run K-Means Clustering in Python, K-means clustering - Wikipedia
Which of the following describes a neural network without an activation function?
A neural network without an activation function is equivalent to a form of a linear regression. A neural network is a computational model that consists of layers of interconnected nodes (neurons) that process inputs and produce outputs. An activation function is a function that determines the output of a neuron 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. Without an activation function, a neural network becomes a linear combination of inputs and weights, which is essentially a linear regression model.
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