Question 1

A multinational company deploys a customer service chatbot that frequently fails to respond accurately in non-English languages. What AI adoption challenges are involved in this scenario?

Options :

A : Scalability issues

B : Data quality and preprocessing

C : Language-specific training limitations

D : GPU memory limits

Answer: B,C

Question 2

A financial services firm is using AI to evaluate creditworthiness, but regulators are concerned about potential bias in loan approvals. What technique should be applied to ensure fairness in AI-driven decisions?

Options :

A : Hyperparameter Tuning, as adjusting parameters improves fairness in decision outcomes

B : Bias Mitigation, as it focuses on identifying and reducing unfair treatment in AI models

C : Feature Engineering, as modifying input features ensures a fair credit evaluation

D : Model Compression, since optimizing AI model size improves efficiency and fairness

Answer: B

Question 3

An attacker sends carefully crafted queries to a language model with the goal of reconstructing the training dataset, including sensitive records. Which threat does this represent?

Options :

A : Input fuzzing

B : Model inversion attack

C : Gradient leakage

D : Adversarial sampling

Answer: B

Question 4

A startup adds differentially private noise to model gradients before sending them to a central server during federated learning. What specific threat is this technique defending against?

Options :

A : Data drift

B : Model inversion

C : Label flipping

D : Adversarial evasion

Answer: B

Question 5

An e-commerce platform's recommendation system begins suggesting inappropriate items to users. Upon inspection, analysts find the model was influenced by manipulated user profiles injected by an external source. What vulnerability is being exploited?

Options :

A : Model distillation

B : Shadow model attack

C : Poisoned user behavior data

D : Output truncation

Answer: C

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