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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. While working on generating a concise response to a user prompt, you notice that the generative AI model in IBM watsonx is producing excessively long outputs. You want to ensure that the response is informative but doesn't exceed a specific length.
Which of the following parameters should you adjust, and what is the most appropriate value to achieve a concise output without cutting off essential information?

A) Set max_tokens to 1000
B) Set max_tokens to 10
C) Set max_tokens to 5
D) Set max_tokens to 100

2. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A) Switch to using deterministic (greedy) decoding to ensure more consistent outputs
B) Lower the temperature to reduce randomness in the model's response.
C) Use a prompt that explicitly asks for neutrality across demographic groups.
D) Incorporate additional training data from underrepresented demographic groups.

3. In the context of AI governance, what is the most important aspect of managing model performance in a production environment to ensure compliance with regulatory and ethical guidelines?

A) Ensuring traceability of model decisions and providing auditability for each inference
B) Maximizing the number of datasets the model is trained on to cover more use cases
C) Minimizing the model's inference time to optimize user experience
D) Deploying the model only in secure, on-premises environments to prevent data breaches

4. A data scientist is choosing between using hard prompts and soft prompts in a generative AI project.
Which of the following best explains why hard prompts might be more suitable for scenarios where explainability is crucial?

A) Hard prompts are based on learned embeddings, which offer better model understanding due to their complexity.
B) Hard prompts reduce the model's flexibility by making the output deterministic, which enhances explainability.
C) Hard prompts allow for a clear, human-readable set of instructions that directly guide the model's behavior.
D) Hard prompts dynamically adjust the model's internal representations, providing more clarity in complex situations.

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

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

Solutions: