IBM C1000-185 : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are fine-tuning a generative AI model in IBM Watsonx and need to define appropriate stopping criteria to ensure the generated text is relevant and coherent.
Which of the following would be an example of a valid stopping criterion for a text generation task?

A) The model will stop generating text once the average probability of each word in the output falls below a 50% threshold.
B) The model will stop generating text once it detects a repetitive sequence of words or phrases, automatically cutting off redundancy.
C) The model will stop generating text when it encounters a special end-of-sequence token or punctuation such as a period or question mark.
D) The model will stop generating text once a predefined number of characters is reached, ensuring that the output does not exceed a set length.

2. You are working with IBM Watsonx to develop a generative AI solution that automatically generates product descriptions for an e-commerce website. The descriptions need to be concise, factual, and include important product features like size, color, and material.
Which prompt design approach would best ensure the output meets these requirements?

A) "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material."
B) "Write a summary that provides information on each product, making the content engaging, humorous, and memorable."
C) "Generate a creative and imaginative product description for the items listed below."
D) "Generate a product description that highlights the unique aspects of the product and uses emotional language to engage the reader."

3. A client needs a Generative AI solution to summarize large legal documents into concise briefs. The solution must capture the critical legal arguments while preserving the formal language required in legal contexts. Additionally, the client wants the model to identify key legal clauses and ensure their inclusion in the summaries. You have a pre-trained LLM that was trained on general text, and now you must design a generative solution to meet the client's needs.
What would be your next step in analyzing and designing the most effective solution?

A) Fine-tune the pre-trained LLM on a dataset of legal documents, specifically focusing on case law, contracts, and formal briefs.
B) Use prompt engineering to instruct the model to focus on key legal clauses and adjust the output to match the legal context.
C) Use a zero-shot approach, prompting the model to summarize legal documents without further fine-tuning.
D) Apply model quantization to optimize the LLM for handling long legal documents more efficiently.

4. You are using a generative AI model in a healthcare application to generate personalized treatment recommendations based on patient data.
Which of the following scenarios represent valid concerns related to model risks when deploying the AI in this setting? (Select two)

A) The model generates text that includes private patient information, violating data privacy regulations.
B) The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
C) The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.
D) The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
E) The model fails to generate treatment recommendations for some patients due to exceeding token limits during inference.

5. You are configuring a chatbot using IBM Watsonx, and you want the chatbot to respond appropriately based on the conversation's context.
Which of the following best represents an appropriate stopping criterion for a task where the chatbot generates step-by-step instructions?

A) The model will stop generating once it encounters a user query that requires it to reevaluate the entire prompt context and restart from the beginning.
B) The model will stop generating once the instruction count reaches five steps, regardless of whether the task has been fully described.
C) The model will stop generating once it identifies a natural completion of the task description, such as reaching a final instruction or a conclusion marker (e.g., "All done").
D) The model will stop generating as soon as the probability of the next token falls below the median probability of previously generated tokens.

Solutions: