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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

A) Using more open-ended prompts
B) Increasing the model's temperature parameter
C) Providing a list of credible sources in the prompt
D) Implementing zero-shot learning techniques

2. During the deployment of a generative AI model using IBM Watsonx, you are responsible for ensuring that the model does not generate content containing hate speech, abusive language, or profanity. However, in some scenarios, the model might bypass basic filtering mechanisms and produce subtle or context-specific harmful language.
Which of the following best addresses edge cases where hate speech or abuse may slip through more basic filtering mechanisms?

A) Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter, which is designed to handle context-specific and subtle forms of harmful language.
B) Use sentiment analysis to detect and block negative content that may indicate hate speech or abuse.
C) Implement regular expression-based filters that cover a wide range of variations in harmful language.
D) Continuously monitor and retrain the model on datasets containing edge cases of hate speech, abuse, and profanity.

3. You are working on a project where the AI model needs to generate personalized customer support responses based on various input fields like customer name, issue type, and product details. To make the system scalable and flexible, you decide to use prompt variables in your implementation.
Which of the following statements accurately describe the benefits of using prompt variables in this scenario? (Select two)

A) Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on-the-fly customization of responses.
B) Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.
C) Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.
D) Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.
E) Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.

4. Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?

A) Allowing the model to auto-correct its own responses by cross-referencing with other outputs
B) Applying Greedy Decoding to ensure the most likely tokens are selected during generation
C) Fine-tuning the model using training data that explicitly includes examples of biased outputs
D) Using neutral and carefully phrased prompts to avoid triggering biased outputs

5. You are fine-tuning a generative model to generate text-based responses in a customer service chatbot. You want to ensure the responses are concise and relevant, without causing the model to produce overly long or irrelevant output.
Which of the following parameters and stopping criteria would be most effective for achieving this goal?

A) Use greedy decoding with no repetition penalty and a high stopping probability threshold.
B) Set a low top-k value and implement a repetition penalty with a low maximum token limit.
C) Increase the temperature to 1.5 and set a high maximum token limit.
D) Use beam search decoding with a low beam width and a high repetition penalty.

Solutions: