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Latest CAIS Exam Practice Questions

The practice questions for CAIS exam was last updated on 2025-11-20 .

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Question#1

What is a common challenge in cross-functional AI teams and how can it be mitigated?

A. Different work cultures; mitigate by establishing clear guidelines

B. Limited project scope; mitigate by expanding project goals

C. Lack of technical skills; mitigate by focusing on business needs

D. Overlapping responsibilities; mitigate by assigning clear roles

Explanation:
Overlapping responsibilities is a common challenge in cross-functional AI teams. This can be mitigated by assigning clear roles and responsibilities, ensuring that each team member understands their specific tasks and contributions, which helps in reducing conflicts and improving project efficiency.

Question#2

Which strategy is often used to fine-tune large language models for specific tasks while maintaining their generalization capabilities?

A. Reducing the number of layers during fine-tuning

B. Increasing the model's parameter count during fine-tuning

C. Training the model from scratch on the specific task's dataset

D. Using transfer learning techniques like domain adaptation

Explanation:
Transfer learning techniques like domain adaptation are often used to fine-tune large language models for specific tasks while maintaining their generalization capabilities. This approach leverages the knowledge the model has already acquired during pre-training, allowing it to adapt efficiently to new tasks.

Question#3

What is the main challenge addressed by Graph Neural Networks in contrast to traditional machine learning methods?

A. Learning from structured graph data

B. Image and video analysis

C. High-dimensional data processing

D. Sequential data modeling

Explanation:
Graph Neural Networks address the challenge of learning from structured graph data, which traditional machine learning methods struggle with. GNNs are designed to work with complex graph structures and capture dependencies between nodes effectively.

Question#4

Which evaluation metric is most appropriate for assessing the performance of a model on an imbalanced binary classification problem?

A. Area Under the ROC Curve (AUC-ROC)

B. Receiver Operating Characteristic (ROC) Curve

C. Precision-Recall Curve (PRC)

D. Mean Squared Error (MSE)

Explanation:
In imbalanced binary classification problems, the Precision-Recall Curve (PRC) is more informative than the ROC Curve. PRC focuses on the positive class, which is crucial when the positive class is rare. It provides insights into precision and recall across different thresholds, giving a clearer picture of model performance in these scenarios.

Question#5

1.How does ChatGPT handle the context of a conversation to provide coherent responses?

A. Through convolutional filters

B. By using recurrent layers

C. By leveraging a dynamic memory system that captures and stores conversation history across multiple interactions

D. By maintaining context within token embeddings

Explanation:
ChatGPT maintains context within token embeddings, allowing it to understand and generate responses that are coherent and relevant to the ongoing conversation, even over multiple exchanges.

Exam Code: CAISQ & A: 540 Q&AsUpdated: 2025-11-20

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