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If you want to demonstrate your expertise in solving complex CertNexus real-life problems, then you need to pass the CertNexus AIP-210 certification exam. However, passing this exam is not an easy task. It requires you to master complicated subjects related to CertNexus Certified Artificial Intelligence Practitioner (CAIP). To help you prepare for this exam, DumpsActual offers verified CertNexus AIP-210 Exam Questions that are ruling the preparation world.

CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 2
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 3
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 4
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following is NOT an activation function?

Answer: C

Explanation:
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.


NEW QUESTION # 52
Which of the following items should be included in a handover to the end user to enable them to use and run a trained model on their own system? (Select three.)

Answer: C,D,E

Explanation:
A handover is the process of transferring the ownership and responsibility of an ML system from one party to another, such as from the developers to the end users. A handover should include all the necessary information and resources that enable the end users to use and run a trained model on their own system. Some of the items that should be included in a handover are:
* Link to a GitHub repository of the codebase: A GitHub repository is an online platform that hosts the source code and version control of an ML system. A link to a GitHub repository can provide the end users with access to the latest and most updated version of the codebase, as well as the history and documentation of the changes made to the code.
* README document: A README document is a text file that provides an overview and instructions for an ML system. A README document can include information such as the purpose, features, requirements, installation, usage, testing, troubleshooting, and license of the system.
* Sample input and output data files: Sample input and output data files are data files that contain examples of valid inputs and expected outputs for an ML system. Sample input and output data files can help the end users understand how to use and run the system, as well as verify its functionality and performance.


NEW QUESTION # 53
Which of the following can take a question in natural language and return a precise answer to the question?

Answer: A

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 # 54
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?

Answer: D

Explanation:
Explanation
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.


NEW QUESTION # 55
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?

Answer: B

Explanation:
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 # 56
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