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The dumps for RAI exam was last updated on Oct 10,2025 .

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Question#1

Which term best describes a dataset that includes a mix of organized, structured financial data and unstructured text data from customer reviews?

A. Structured data
B. Unstructured data
C. Semi-structured data
D. Binary data

Explanation:
Correct (C): Semi-structured data combines structured and unstructured elements, such as financial data with unstructured customer reviews.
Incorrect (A): Structured data is entirely organized in a set format without unstructured components.
Incorrect (B): Unstructured data lacks an organized format and does not include structured elements.
Incorrect (D): Binary data only represents attributes with two values and does not describe mixed data types.

Question#2

A credit risk model using a neural network shows a large gap between training and test error.
Which of the following techniques would be most effective in addressing this issue?

A. Increase the learning rate
B. Use early stopping
C. Increase the number of layers
D. Reduce the batch size

Explanation:
B is correct. Early stopping prevents overfitting by halting training before the model perfectly fits the training data, which often generalizes better to test data
A is incorrect because increasing the learning rate may cause instability in training
C is incorrect as adding layers may exacerbate overfitting
D is incorrect as batch size does not directly address overfitting.

Question#3

A portfolio manager is using reinforcement learning to maximize returns over time.
To evaluate each policy, he wants to calculate the long-term value of returns using the Bellman equation, considering both immediate rewards and discounted future rewards.
Which function does he use to achieve this?

A. Policy function
B. Reward function
C. Value function V(s)
D. State-action function Q(s,a)

Explanation:
C is correct. The value function V(s) measures the expected cumulative future rewards, considering immediate and discounted future rewards, as described by the Bellman equation.
A is incorrect, as the policy function maps actions to states but doesn’t evaluate value
B is incorrect since it only provides immediate rewards without discounting future returns
D is incorrect as Q(s,a) calculates the action-value, not the state value.

Question#4

A tech firm is evaluating different models for an NLP project requiring high computational efficiency and parallel processing.
Why might transformers be more suitable than RNNs for this task?

A. They use contextual embeddings
B. They are sequence-dependent
C. They allow for parallel processing
D. They rely on sequence ordering

Explanation:
C is correct. Transformers enable parallel processing, making them computationally efficient, especially for large text datasets
A is incorrect because although transformers use contextual embeddings, this does not relate directly to computational efficiency
B is incorrect as RNNs, not transformers, are sequence- dependent
D is incorrect as transformers can handle sequences without a strict order due to the attention mechanism.

Question#5

In a Q-Q plot comparing an empirical distribution to a theoretical normal distribution, what indicates a close match between the two?

A. Data points are widely scattered
B. Data points follow a linear path along y=x
C. Data points are randomly distributed
D. Data points are concentrated near the origin

Explanation:
Correct (B): When data points lie along the line y=x, it indicates the empirical and theoretical distributions are similar.
Incorrect (A): Scattered points suggest a poor fit.
Incorrect (C): Random distribution of points does not indicate a normal distribution match.
Incorrect (D): Concentration near the origin does not indicate fit and may imply outliers.

Exam Code: RAI         Q & A: 330 Q&As         Updated:  Oct 10,2025

 

 Full RAI Exam Dumps Here

Exam Code: RAI
Q & A: 330 Q&As
Updated:  Oct 10,2025

 

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