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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	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Topic 3	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following best describes distributed artificial intelligence?

A. It does not require hyperparemeter tuning because the distributed nature accounts for the bias.
B. It relies on a distributed system that performs robust computations across a network of unreliable nodes.
C. It uses a centralized system to speak to decentralized nodes.
D. It intelligently pre-distributes the weight of starting a neural network.

Answer: B

Explanation:
Explanation
Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that studies how multiple intelligent agents can coordinate and cooperate to achieve a common goal or solve a complex problem. DAI relies on a distributed system that performs robust computations across a network of unreliable nodes, such as sensors, robots, or humans. DAI can handle large-scale, dynamic, and uncertain environments that are beyond the capabilities of a single agent. References: [Distributed artificial intelligence - Wikipedia], [Distributed Artificial Intelligence: An Overview]

NEW QUESTION # 25
For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy. Which is the best algorithm for this use case?

A. K-nearest neighbors
B. Deep neural network
C. SVM
D. Random forest

Answer: D

Explanation:
Explanation
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature's relative contribution to the model's accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .

NEW QUESTION # 26
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?

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

Answer: B

Explanation:
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 # 27
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?

A. When data drift is detected
B. Periodically before New Year's Day and after New Year's Day
C. When concept drift is detected
D. Periodically every year

Answer: D

NEW QUESTION # 28
Which of the following describes a typical use case of video tracking?

A. Video composition
B. Medical diagnosis
C. Augmented dreaming
D. Traffic monitoring

Answer: D

Explanation:
Video tracking is a technique that involves detecting and following moving objects in a video sequence.
Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human- computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.

NEW QUESTION # 29
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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q24-Q29):

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