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When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?
A. Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
B. Greedy decoding with no stop sequence and maximum tokens set to 200.
C. Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".
D. Greedy decoding with temperature set to 2.0 and no stop sequence.
Answer: A
NEW QUESTION # 165
What is a key advantage of using prompt variables in IBM Watsonx for a chatbot application that needs to handle multiple user intents?
A. Prompt variables enable developers to predefine multiple static responses for each possible user input.
B. Prompt variables allow the model to automatically detect the user's intent, ensuring accurate responses.
C. Using prompt variables ensures that the model will always select the most relevant response based on past conversations.
D. Prompt variables allow for the dynamic injection of user-specific details into the response, improving personalization.
Answer: D
NEW QUESTION # 166
You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?
A. Increase the length of the input prompt to ensure that responses are more complete.
B. Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.
C. Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
D. Lower the model's perplexity score to improve both completeness and factual accuracy.
Answer: C
NEW QUESTION # 167
A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?
A. Switching to greedy decoding for more deterministic responses
B. Using human-in-the-loop (HITL) methods for real-time validation
C. Increasing the model's response diversity by adjusting top-p sampling
D. Deploying rule-based post-processing filters to validate the output
Answer: D
NEW QUESTION # 168
You are working with a healthcare provider to deploy a generative AI model that assists in diagnosing patient conditions based on medical history and symptoms. The healthcare provider is concerned about ethical use, bias, and compliance with health data regulations such as HIPAA.
Which governance approach is the most critical to ensure both compliance and ethical AI deployment?
A. Use a cloud-based infrastructure with HIPAA certification to deploy the model, ensuring compliance with healthcare regulations.
B. Apply a federated learning approach to train the model without directly accessing the healthcare provider's patient data.
C. Implement a bias detection framework to identify and mitigate any biases present in the model's outputs.
D. Ensure data encryption during model inference to protect patient information from unauthorized access.
Answer: C
NEW QUESTION # 169
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