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CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 2
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 3
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 4
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 5
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which of the following describes a typical use case of video tracking?

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 # 91
Which of the following is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data?

Answer: C

Explanation:
Explanation
The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.


NEW QUESTION # 92
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: C

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 # 93
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?

Answer: A


NEW QUESTION # 94
Which of the following scenarios is an example of entanglement in ML pipelines?

Answer: A

Explanation:
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.


NEW QUESTION # 95
......

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