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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. How can developers leverage Prompt Lab in IBM Watsonx to build reusable prompts for a conversational AI system that handles multiple topics?

A) By utilizing Prompt Lab's automatic suggestion feature, which builds new prompts based on the system's training data.
B) By designing prompts with dynamic variables for user-specific information, allowing for flexibility across different topics.
C) By creating a single prompt template that adapts to all user inputs regardless of the subject.
D) By generating a unique prompt for each possible user query to ensure maximum precision.

2. 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) Deploying rule-based post-processing filters to validate the output
B) Switching to greedy decoding for more deterministic responses
C) Increasing the model's response diversity by adjusting top-p sampling
D) Using human-in-the-loop (HITL) methods for real-time validation

3. In what situation might greedy decoding fail to generate an optimal output, even though it consistently chooses the most probable token at each step?

A) Greedy decoding guarantees the highest overall probability for the output sequence
B) Greedy decoding is highly effective when multiple equally probable tokens are available at each step
C) Greedy decoding maximizes local probabilities but can lead to suboptimal global coherence
D) Greedy decoding works best when combined with temperature scaling to increase randomness

4. When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?

A) Training the model on a diverse and representative dataset
B) Incorporating fairness constraints during the model's training phase
C) Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
D) Using temperature control during generation to manage diversity in responses

5. 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 use of a greedy decoding strategy without beam search.
C) The model's training on incomplete or unstructured datasets leading to incorrect generalizations.
D) The model's over-reliance on token repetition to form coherent sentences.

Solutions: