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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q288-Q293):

NEW QUESTION # 288
A financial institution is using a generative AI model to create reports based on transaction data. During deployment, the institution notices that the model sometimes fabricates trends or patterns that do not exist in the underlying data. This is an example of a hallucination.
Which of the following techniques would best minimize this risk during inference?

A. Disable the model's autoregressive capability to prevent it from generating future predictions.
B. Increase the top-p value to ensure more tokens are considered during generation.
C. Reduce the model size to decrease its capacity to hallucinate complex patterns.
D. Use a retrieval-augmented generation (RAG) model that incorporates external financial data into the generation process.

Answer: D

NEW QUESTION # 289
How can developers leverage Prompt Lab in IBM Watsonx to build reusable prompts for a conversational AI system that handles multiple topics?

A. By creating a single prompt template that adapts to all user inputs regardless of the subject.
B. By generating a unique prompt for each possible user query to ensure maximum precision.
C. By designing prompts with dynamic variables for user-specific information, allowing for flexibility across different topics.
D. By utilizing Prompt Lab's automatic suggestion feature, which builds new prompts based on the system's training data.

Answer: C

NEW QUESTION # 290
You are working with IBM Watsonx to generate automated customer support responses. To ensure consistency and flexibility in responses across multiple product categories, you decide to use prompt variables.
Which of the following best describes the benefits of using prompt variables in this scenario?

A. They enable the AI to better predict the intent of the customer query, reducing the need for explicit customer input.
B. They allow for dynamic input fields in prompts, making it easier to tailor responses for different product categories without rewriting the entire prompt.
C. They ensure that the AI generates responses with consistent tone and personality across all prompts, regardless of product category.
D. They automatically improve the accuracy of the AI's responses by allowing the system to learn from each generated prompt.

Answer: B

NEW QUESTION # 291
You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.
To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

A. 32-bit floating point quantization with fine-tuning
B. Post-training 8-bit integer quantization
C. Post-training 16-bit floating point quantization
D. Binary quantization (1-bit)

Answer: B

NEW QUESTION # 292
When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?

A. Increasing the temperature parameter to 1.5
B. Reducing the beam size in beam search from 5 to 1
C. Using Top-k Sampling with a k value of 100
D. Decreasing the value of the temperature parameter to 0.2

Answer: D

NEW QUESTION # 293
......

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