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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

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

Topic 2

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

Topic 3

Identify potential ethical concerns
Analyze machine learning system use cases

Topic 4

Transform numerical and categorical data
Address business risks, ethical concerns, and related concepts in operationalizing the model

Topic 5

Design machine and deep learning models
Explain data collection
transformation process in ML workflow

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which of the following is TRUE about SVM models?

A. They can take the feature space into higher dimensions to solve the problem.
B. They can be used only for classification.
C. They use the sigmoid function to classify the data points.
D. They can be used only for regression.

Answer: A

Explanation:
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia

NEW QUESTION # 75
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?

A. Bagging
B. Boosting
C. Weighted class
D. Stacking

Answer: C

Explanation:
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog

NEW QUESTION # 76
Which two of the following decrease technical debt in ML systems? (Select two.)

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

Answer: B,D

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 # 77
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)

A. Population variance
B. Population regression
C. Population estimates
D. Population resampling
E. Population triage

Answer: C,E

Explanation:
Explanation
Personas are fictional characters that represent the potential users or customers of an ML system. Personas can help understand the needs, goals, preferences, and behaviors of the target audience, as well as design and evaluate the system from their perspective. Some of the techniques that are used to build personas in the ML development lifecycle are:
Population estimates: Population estimates are statistical methods that estimate the size, characteristics, and distribution of a population based on a sample or a census. Population estimates can help identify and quantify the potential market segments and user groups for an ML system, as well as their demographics, locations, and behaviors.
Population triage: Population triage is a process of prioritizing and selecting the most relevant and representative personas for an ML system based on some criteria or metrics. Population triage can help focus on the key user needs and scenarios, as well as avoid creating too many or too few personas.

NEW QUESTION # 78
When should the model be retrained in the ML pipeline?

A. Some outliers are detected in live data.
B. A new monitoring component is added.
C. Concept drift is detected in the pipeline.
D. More data become available for the training phase.

Answer: C

Explanation:
Explanation
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing

NEW QUESTION # 79
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