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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q236-Q241):

NEW QUESTION # 236
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. Increase the top-p value to ensure more tokens are considered during generation.
B. Disable the model's autoregressive capability to prevent it from generating future predictions.
C. Use a retrieval-augmented generation (RAG) model that incorporates external financial data into the generation process.
D. Reduce the model size to decrease its capacity to hallucinate complex patterns.

Answer: C

NEW QUESTION # 237
You are working on generating creative text responses using IBM watsonx's generative AI model. You need to adjust the output so that it is more diverse and creative without losing coherence.
Which of the following model parameter settings would best achieve this objective?

A. Set temperature to 0.2
B. Set temperature to 2
C. Set temperature to 0
D. Set temperature to 1.5

Answer: D

NEW QUESTION # 238
You are tasked with designing a prompt template to assist a chatbot in generating professional email responses for customer service inquiries. The system should prioritize politeness, clarity, and conciseness.
What elements should be included in the prompt template to achieve the best results, considering optimal behavior of a large language model (LLM)? (Select two)

A. Ask the model to generate multiple versions of the response and rank them
B. Specify the output tone as polite and professional
C. Include examples of informal customer service responses for variability
D. Instruct the model to limit responses to a specific character count
E. Provide the customer's emotional context for better alignment with the tone

Answer: B,D

NEW QUESTION # 239
You are implementing a few-shot prompting strategy with IBM Watsonx to improve the model's performance in generating customer service responses. The goal is to ensure the model understands the tone and format required for polite and concise replies.
Which of the following strategies best illustrates the correct way to use few-shot prompting?

A. Provide example prompts with multiple different output styles to give the model a range of responses to choose from.
B. Use only negative examples in the prompt to show the model what not to generate in terms of tone and format.
C. Include a large number of examples, typically over 10, in the input prompt to ensure the model learns from diverse cases.
D. Provide one or two well-structured examples that demonstrate the expected tone and format of the customer service responses within the prompt.

Answer: D

NEW QUESTION # 240
Which of the following techniques can be most effectively used to mitigate the generation of hate speech, abuse, and profanity in generative AI models when applying prompt engineering?

A. Applying Token Regularization to limit the diversity of generated responses
B. Fine-tuning the model with specific datasets curated to exclude offensive content
C. Using Greedy Decoding to ensure that the model outputs the most likely sequence of tokens
D. Restricting the model's ability to generate certain words or phrases using stop-word lists

Answer: B

NEW QUESTION # 241
......

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C1000-185 Actual Torrent: IBM watsonx Generative AI Engineer - Associate - C1000-185 Pass-King Materials & C1000-185 Actual Exam

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q236-Q241):

C1000-185 perp training & C1000-185 testking vce & C1000-185 valid torrent

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