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Free PDF 2026 CertNexus AIP-210: CertNexus Certified Artificial Intelligence Practitioner (CAIP) Fantastic Certification Sample Questions

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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

Train, validate, and test data subsets
Training and Tuning ML Systems and Models

Topic 2

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

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

Topic 5

Identify potential ethical concerns
Analyze machine learning system use cases

Topic 6

Address business risks, ethical concerns, and related concepts in training and tuning
Work with textual, numerical, audio, or video data formats

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which of the following is NOT an activation function?

A. Sigmoid
B. Hyperbolic tangent
C. Additive
D. ReLU

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 # 65
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?

A. k-means, because this is a clustering problem with a small number of features.
B. Logistic regression, because this is a classification problem and our data is linearly separable.
C. A decision tree algorithm, because the problem is a classification problem with a small number of features.
D. Neural network model, because this is a classification problem with a large number of features.

Answer: D

Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]

NEW QUESTION # 66
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?

A. Reduction in latency
B. Guaranteed availability of enough space
C. Increase in data bandwidth consumption
D. A more complex model

Answer: A

Explanation:
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.

NEW QUESTION # 67
Word Embedding describes a task in natural language processing (NLP) where:

A. Words are featurized by taking a matrix of bigram counts.
B. Words are grouped together into clusters and then represented by word cluster membership.
C. Words are converted into numerical vectors.
D. Words are featurized by taking a histogram of letter counts.

Answer: C

Explanation:
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
* One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
* Word2vec: Word2vec is a method that learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations.
* GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.

NEW QUESTION # 68
In general, models that perform their tasks:

A. Less accurately are less robust against adversarial attacks.
B. More accurately are less robust against adversarial attacks.
C. Less accurately are neither more nor less robust against adversarial attacks.
D. More accurately are neither more nor less robust against adversarial attacks.

Answer: B

Explanation:
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]

NEW QUESTION # 69
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