Free GARP RAI Exam Questions
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GARP RAI Exam Questions
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Provided By: GARP
- Exam: Risk and AI (RAI)
- Certification: GARP Certification
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Total Questions: 330
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Updated On: Apr 25, 2025
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Rated: 4.9 |
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Online Users: 660
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Question 1
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Which type of risk might an organization face if an AI algorithm breaches individual privacy or yields biased results?
Answer: C
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Which type of risk might an organization face if an AI algorithm breaches individual privacy or yields biased results?
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Question 2
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In an effort to understand why certain loan applications were denied, a bank deploys LIME (local interpretable model-agnostic explanations). What is the primary advantage of using LIME?
Answer: B
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In an effort to understand why certain loan applications were denied, a bank deploys LIME (local interpretable model-agnostic explanations). What is the primary advantage of using LIME?
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Question 3
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During training, an RNN model used for forecasting exhibits extremely high gradients at certain steps, causing instability in learning. Which of the following best describes this problem?
Answer: B
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During training, an RNN model used for forecasting exhibits extremely high gradients at certain steps, causing instability in learning. Which of the following best describes this problem?
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Question 4
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An insurance company is using a neural network for classifying claims as "Fraudulent" or "Non-Fraudulent." They decide to use ReLU (Rectified Linear Unit) as the activation function for the hidden layers. What is the primary purpose of using an activation function like ReLU in this neural network?
Answer: B
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An insurance company is using a neural network for classifying claims as "Fraudulent" or "Non-Fraudulent." They decide to use ReLU (Rectified Linear Unit) as the activation function for the hidden layers. What is the primary purpose of using an activation function like ReLU in this neural network?
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Question 5
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While using Naïve Bayes for text classification, a bank wants to classify customer feedback as “Good” or “Bad.” How does the model handle the probability of classifying feedback with words it hasn’t encountered?
Answer: D
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While using Naïve Bayes for text classification, a bank wants to classify customer feedback as “Good” or “Bad.” How does the model handle the probability of classifying feedback with words it hasn’t encountered?