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[General] CertNexus - Professional Reliable AIP-210 Test Cost

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【General】 CertNexus - Professional Reliable AIP-210 Test Cost

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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
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
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
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 5
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 6
  • 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 (Q27-Q32):NEW QUESTION # 27
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
  • A. If it is for a good cause.
  • B. If it was requested by the authority of law.
  • C. If the data is only collected once.
  • D. If it was with consent of the person it is collected from.
  • E. If it was collected accidentally.
Answer: B,D
Explanation:
Explanation
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.

NEW QUESTION # 28
Which of the following is the primary purpose of hyperparameter optimization?
  • A. Makes models easier to explain to business stakeholders
  • B. Controls the learning process of a given algorithm
  • C. Improves model interpretability
  • D. Increases recall over precision
Answer: B
Explanation:
Explanation
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.

NEW QUESTION # 29
You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
  • A. When it is necessary to save computational time.
  • B. When the categories of the dependent variable are not linearly separable.
  • C. When the distribution of the dependent variable is Gaussian.
  • D. When there is high correlation among the features.
Answer: B
Explanation:
Explanation
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.

NEW QUESTION # 30
Normalization is the transformation of features:
  • A. By subtracting from the mean and dividing by the standard deviation.
  • B. So that they are on a similar scale.
  • C. To different scales from each other.
  • D. Into the normal distribution.
Answer: B
Explanation:
Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or
-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k- nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization - Quantitative analysis]

NEW QUESTION # 31
Which of the following is the correct definition of the quality criteria that describes completeness?
  • A. The degree to which the measures conform to defined business rules or constraints.
  • B. The degree to which all required measures are known.
  • C. The degree to which a set of measures are equivalent across systems.
  • D. The degree to which a set of measures are specified using the same units of measure in all systems.
Answer: B
Explanation:
Explanation
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.

NEW QUESTION # 32
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