Question 1

A retail bank uses self-training to classify loan applicants as high or low risk, but it finds that updating the model after each new labeled data point is computationally intensive. Which approach can the bank use to reduce this burden?

Options :

A : Select multiple best-predicted instances in each round

B : Use an unsupervised learning model instead

C : Increase the complexity of the initial model

D : Use transductive learning instead

Answer: A

Question 2

The CEO is concerned about the societal impact of a new AI-driven social media algorithm that recommends content. From a practical ethics viewpoint, what approach should the company take?

Options :

A : Design the algorithm to prioritize user engagement above all

B : Incorporate stakeholder feedback to align the algorithm with societal values

C : Allow the algorithm to operate without restrictions to maximize revenue

D : Use only deontological principles in its design for strict ethical compliance

Answer: B

Question 3

A bank is validating a model for credit risk analysis. What should be the primary focus of the validation process to ensure its effectiveness?

Options :

A : Assess if the model is appropriate and effectively used for its intended purpose.

B : Confirm that the model is perfectly accurate under all scenarios.

C : Test the model only once before implementation.

D : Ensure the model uses only proprietary data.

Answer: A

Question 4

A retail company wants to categorize thousands of product images quickly with limited computational resources. They decide to use a CNN with pooling layers. What is the primary benefit of including pooling layers in their CNN model?

Options :

A : Increases the model-s accuracy

B : Reduces computational requirements

C : Helps retain high-level details

D : Prevents overfitting by regularization

Answer: B

Question 5

A financial institution is developing a credit risk model using a machine learning algorithm. The data science team decides to split their dataset into three parts: training, validation, and test sets. What is the primary purpose of the validation set in this setup?

Options :

A : To finalize the model by evaluating it on unseen data

B : To select the best performing model among multiple algorithms

C : To adjust model parameters to fit the training data

D : To test the model’s performance under different scenarios

Answer: B

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