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Title: AIP-210 Sample Test Online & Valid Dumps AIP-210 Pdf [Print This Page]
Author: lougree934    Time: yesterday 21:21
Title: AIP-210 Sample Test Online & Valid Dumps AIP-210 Pdf
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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 2
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 3
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 4
  • 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 (Q14-Q19):NEW QUESTION # 14
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?
Answer: A
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 # 15
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
Answer: A,D
Explanation:
Explanation
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.

NEW QUESTION # 16
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
Answer: A
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.

NEW QUESTION # 17
Which of the following can take a question in natural language and return a precise answer to the question?
Answer: C
Explanation:
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.

NEW QUESTION # 18
Which of the following tests should be performed at the production level before deploying a newly retrained model?
Answer: A
Explanation:
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. References: [Performance Testing Tutorial: What is, Types, Metrics and Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]

NEW QUESTION # 19
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