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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

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. A decision tree algorithm, because the problem is a classification problem with a small number of features.
• C. Logistic regression, because this is a classification problem and our data is linearly separable.
• 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 # 49
Which two of the following decrease technical debt in ML systems? (Select two.)

• A. Refactoring
• B. Design anti-patterns
• C. Boundary erosion
• D. Model complexity
• E. Documentation readability

Answer: A,E

Explanation:
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
* Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
* Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.

NEW QUESTION # 50
Which of the following tools would you use to create a natural language processing application?

• A. Azure Search
• B. NLTK
• C. DeepDream
• D. AWS DeepRacer

Answer: B

Explanation:
Explanation
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .

NEW QUESTION # 51
Which of the following equations best represent an LI norm?

• A. |x|-|y|
• B. |x| + |y|
• C. |x|

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