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CertNexus AIP-210 Exam Dumps

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Description

Exam Name: Certified Artificial Intelligence Practitioner Exam
Exam Code: AIP-210 CAIP
Related Certification(s): CertNexus Certified AI Practitioner Certification
Certification Provider: CertNexus
Actual Exam Duration: 120 Minutes
Number of AIP-210 practice questions in our database: 90

Expected AIP-210 Exam Topics, as suggested by CertNexus :

  • Module 1: Identify potential ethical concerns/ Analyze machine learning system use cases
  • Module 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
  • Module 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
  • Module 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
  • Module 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
  • Module 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
  • Module 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow

Description

Exam Name: Certified Artificial Intelligence Practitioner Exam
Exam Code: AIP-210 CAIP
Related Certification(s): CertNexus Certified AI Practitioner Certification
Certification Provider: CertNexus
Actual Exam Duration: 120 Minutes
Number of AIP-210 practice questions in our database: 90

Expected AIP-210 Exam Topics, as suggested by CertNexus :

  • Module 1: Identify potential ethical concerns/ Analyze machine learning system use cases
  • Module 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
  • Module 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
  • Module 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
  • Module 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
  • Module 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
  • Module 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow

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Q1. You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)

A.K-means

B. K-nearest neighbors

C. Lasso regression

D. Logistic regression

E. Ridge regression

Correct Answer: C, E

Q2. Which of the following tests should be performed at the production level before deploying a newly retrained model?

A.A/Btest

B. Performance test

C. Security test

D. Unit test

Correct Answer: B

Q3. You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)

A.K-means

B. K-nearest neighbors

C. Lasso regression

D. Logistic regression

E. Ridge regression

Correct Answer: C, E

Q4. 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.

Correct Answer: B

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