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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?

A) Limit the model's context window to prevent it from over-relying on demographic information.
B) Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
C) Use adversarial debiasing techniques to adjust the model's internal representations during training.
D) Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.

2. In the context of IBM Watsonx Generative AI models, hallucinations refer to outputs where the model generates text that is factually incorrect or not grounded in the provided input or training data. Understanding the underlying causes of hallucinations is critical for maintaining the reliability of the model.
Which of the following best describes a primary cause of hallucinations in generative models?

A) The model's incapacity to follow the temperature parameter settings.
B) The model's over-reliance on token repetition to form coherent sentences.
C) The model's training on incomplete or unstructured datasets leading to incorrect generalizations.
D) The model's use of a greedy decoding strategy without beam search.

3. When using Tuning Studio in IBM watsonx, which of the following types of models is most suitable for fine-tuning in a domain-specific task to optimize performance?

A) A model trained from scratch on a small, specific dataset.
B) A pre-trained model with very few layers to ensure fast processing speeds.
C) An untrained model with a minimal number of layers to reduce complexity.
D) A pre-trained model that has been trained on a large, general-purpose dataset.

4. In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages.
Which of the following best describes when to use a vector database as part of the Retrieval-Augmented Generation (RAG) approach?

A) When the dataset consists mainly of structured tabular data and relational queries.
B) When there is a requirement to process large volumes of streaming data in real-time, and exact matching is the priority.
C) When the data consists of diverse unstructured documents, and you need to retrieve semantically similar content using dense vector representations.
D) When there is a need to perform efficient keyword-based search on highly structured documents.

5. You are tasked with designing a prompt to fine-tune an IBM Watsonx model to summarize legal documents. The summaries must include only factual information, highlight key legal terms, and exclude any personal interpretations or subjective analysis.
Which of the following is the best prompt to achieve this goal?

A) "Summarize this legal document, focusing on key arguments and providing an analysis of the potential outcomes of the case."
B) "Create a brief summary of this legal document, ensuring to exclude any legal jargon and simplifying the content for a layperson audience."
C) "Generate a detailed and engaging summary of this legal document, adding your insights to clarify complex legal points for the reader."
D) "Provide a summary of this legal document, focusing on factual information, including key legal terms and avoiding personal interpretation or subjective analysis."

Solutions: