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C1000-185 Prepaway Dumps - 100% Pass Quiz First-grade IBM C1000-185 - Original IBM watsonx Generative AI Engineer - Associate Questions

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q218-Q223):

NEW QUESTION # 218
You are designing a prompt template for IBM Watsonx to generate various types of content for a company's internal communications. The goal is for the template to be reusable across different formats (emails, announcements, reports).
Which of the following best describes an effective template for this scenario?

A. ":Generate a formal communication piece for the company."
B. "Produce a report with all necessary information."
C. "Create a [content type] for [audience], including key points on [subject]. Use a [tone] style and keep the length around [length]."
D. "Write the content for the communication based on the provided information."

Answer: C

NEW QUESTION # 219
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 fails to generate treatment recommendations for some patients due to exceeding token limits during inference.
B. The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
C. The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
D. The model generates text that includes private patient information, violating data privacy regulations.
E. The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.

Answer: D,E

NEW QUESTION # 220
In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

A. One-Hot Encoding
B. Word2Vec
C. Bag-of-Words (BoW)
D. Latent Dirichlet Allocation (LDA)

Answer: B

NEW QUESTION # 221
You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

A. Convert all text to lowercase to ensure uniformity in the dataset.
B. Manually categorize each interaction and organize them into a taxonomy tree structure.
C. Tokenize the dataset before curating it and mapping it to the taxonomy tree.
D. Perform a spelling correction on the entire dataset to remove any language inconsistencies.

Answer: B

NEW QUESTION # 222
A generative AI team is optimizing a model for text summarization. The team uses both hard prompts and soft prompts during training.
What is the primary reason a soft prompt might provide less explainability compared to a hard prompt?

A. Soft prompts involve complex embedding vectors, which are learned and difficult for humans to interpret directly.
B. Soft prompts include manually designed templates that change dynamically, reducing transparency.
C. Soft prompts are explicitly written by humans and include domain-specific instructions, making them harder to explain.
D. Soft prompts are used exclusively in unsupervised learning settings, where explainability is inherently lower due to lack of labeled data.

Answer: A

NEW QUESTION # 223
......

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