CA Foundation Business Economics Questions 2023 - Part 32
Fri, 03 Mar 2023
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AWS Certified Machine Learning - Specialty Certification - Part 14
1. What is the objective of back propagation algorithm?
A) To develop learning algorithm for multilayer feed forward neural network, so that network can be trained to capture the mapping implicitly
B) To develop learning algorithm for multilayer feed forward neural network
C) To develop learning algorithm for single layer feed forward neural network
D) All of the above
2. Neural Networks are complex functions with many parameters
A) Linear
B) Non linear
C) Discrete
D) Exponential
3. Which of the following is the consequence between a node and its predecessors while creating Bayesian network?
A) Conditionally independent
B) Functionally dependent
C) Both
D) None of the above
4. The compactness of the bayesian network can be described by
A) Fully structured
B) Locally structured
C) Partially structured
D) All of the above
5. Which of the following is correct about the Naive Bayes?
A) Assumes that all the features in a dataset are independent
B) Assumes that all the features in a dataset are equally important
C) Both
D) All of the above
A) To develop learning algorithm for multilayer feed forward neural network, so that network can be trained to capture the mapping implicitly
B) To develop learning algorithm for multilayer feed forward neural network
C) To develop learning algorithm for single layer feed forward neural network
D) All of the above
2. Neural Networks are complex functions with many parameters
A) Linear
B) Non linear
C) Discrete
D) Exponential
3. Which of the following is the consequence between a node and its predecessors while creating Bayesian network?
A) Conditionally independent
B) Functionally dependent
C) Both
D) None of the above
4. The compactness of the bayesian network can be described by
A) Fully structured
B) Locally structured
C) Partially structured
D) All of the above
5. Which of the following is correct about the Naive Bayes?
A) Assumes that all the features in a dataset are independent
B) Assumes that all the features in a dataset are equally important
C) Both
D) All of the above
1. Right Answer: A
Explanation:
2. Right Answer: A
Explanation:
3. Right Answer: A
Explanation:
4. Right Answer: B
Explanation:
5. Right Answer: C
Explanation:
Explanation:
2. Right Answer: A
Explanation:
3. Right Answer: A
Explanation:
4. Right Answer: B
Explanation:
5. Right Answer: C
Explanation:
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