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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q284-Q289):

NEW QUESTION # 284
While working on generating a concise response to a user prompt, you notice that the generative AI model in IBM watsonx is producing excessively long outputs. You want to ensure that the response is informative but doesn't exceed a specific length.
Which of the following parameters should you adjust, and what is the most appropriate value to achieve a concise output without cutting off essential information?

A. Set max_tokens to 1000
B. Set max_tokens to 100
C. Set max_tokens to 10
D. Set max_tokens to 5

Answer: B

NEW QUESTION # 285
In the context of decoding methods in IBM Watsonx Generative AI, both top-k and top-p sampling are used to control the output of the model.
Which of the following statements correctly distinguishes between top-k and top-p sampling?

A. Top-p sampling allows for a more flexible token selection process based on cumulative probabilities, while top-k limits token selection to a fixed number of top choices.
B. Top-k sampling samples from a dynamic range of tokens, while top-p sampling restricts choices to a fixed number of tokens.
C. Both top-k and top-p sampling ensure that no tokens are selected with a probability below the set threshold, regardless of their context.
D. Top-p sampling ensures deterministic outputs, whereas top-k sampling guarantees random selection from the entire vocabulary.

Answer: A

NEW QUESTION # 286
You are designing a generative AI model to generate detailed technical

A. Stop Sequence = '<END>', Max Tokens = 100
B. Stop Sequence = None, Max Tokens = 700
C. Max Tokens = 500: This provides enough space for a detailed technical
D. Stop Sequence = 'END', Max Tokens = 500

Answer: D

NEW QUESTION # 287
Which two of the following are key benefits of using IBM Watsonx's Tuning Studio for fine-tuning generative AI models on specific tasks? (Select two)

A. Offers real-time validation of tuning experiments to optimize model performance
B. Provides automated hyperparameter search and tuning strategies
C. Automatically generates prompt templates based on data input
D. Significantly reduces the risk of overfitting without manual intervention
E. Automatically selects the best model architecture for the task

Answer: A,B

NEW QUESTION # 288
You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A. Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
B. Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
C. Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6
D. Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3

Answer: C

NEW QUESTION # 289
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