Latest AIP-210 Exam Notes, AIP-210 Latest Mock Test

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CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 2
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 3
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q77-Q82):

NEW QUESTION # 77
Normalization is the transformation of features:

Answer: B

Explanation:
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 # 78
Which of the following is the definition of accuracy?

Answer: B

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 # 79
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

Answer: B

Explanation:
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word "hello" would be represented as
{"he": 1, "el": 1, "ll": 1, "lo": 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as "th" in English or "ch" in German .


NEW QUESTION # 80
Which of the following describes a typical use case of video tracking?

Answer: C

Explanation:
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 # 81
Which of the following is TRUE about SVM models?

Answer: B

Explanation:
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 # 82
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