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[General] Reliable AIP-210 Braindumps - Reliable AIP-210 Test Question

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【General】 Reliable AIP-210 Braindumps - Reliable AIP-210 Test Question

Posted at 1/18/2026 18:55:28      View：68 | Replies：2        Print      Only Author   [Copy Link] 1#
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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q56-Q61):NEW QUESTION # 56
Which of the following is the definition of accuracy?
  • A. (True Positives + True Negatives) / Total Predictions
  • B. True Positives / (True Positives + False Negatives)
  • C. True Positives / (True Positives + False Positives)
  • D. (True Positives + False Positives) / Total Predictions
Answer: A
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).

NEW QUESTION # 57
Which of the following describes a benefit of machine learning for solving business problems?
  • A. Increasing the quantity of original data
  • B. Increasing the speed of analysis
  • C. Improving the constraint of the problem
  • D. Improving the quality of original data
Answer: B
Explanation:
Explanation
Increasing the speed of analysis is a benefit of machine learning for solving business problems. Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and make predictions or decisions. Machine learning can help increase the speed of analysis by automating and optimizing various tasks, such as data processing, feature extraction, model training, model evaluation, or model deployment. Machine learning can also help handle large and complex data sets that may be difficult or impractical to analyze manually or with traditional methods.

NEW QUESTION # 58
Which of the following describes a benefit of machine learning for solving business problems?
  • A. Increasing the quantity of original data
  • B. Increasing the speed of analysis
  • C. Improving the constraint of the problem
  • D. Improving the quality of original data
Answer: B
Explanation:
Increasing the speed of analysis is a benefit of machine learning for solving business problems. Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and make predictions or decisions. Machine learning can help increase the speed of analysis by automating and optimizing various tasks, such as data processing, feature extraction, model training, model evaluation, or model deployment. Machine learning can also help handle large and complex data sets that may be difficult or impractical to analyze manually or with traditional methods.

NEW QUESTION # 59
Which of the following algorithms is an example of unsupervised learning?
  • A. Random forest
  • B. Neural networks
  • C. Principal components analysis
  • D. Ridge regression
Answer: C
Explanation:
Explanation
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.

NEW QUESTION # 60
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 regression.
  • C. They can be used only for classification.
  • D. They use the sigmoid function to classify the data points.
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 # 61
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Posted at 1/20/2026 18:36:24        Only Author  2#
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Posted at 1/23/2026 13:52:34        Only Author  3#
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