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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with optimizing a generative AI model's usage in a chatbot that provides troubleshooting instructions for software issues. The current prompt template is:
"Please provide step-by-step troubleshooting instructions for the following issue: [Issue Description]. Be detailed, include specific commands or settings the user should check, and provide potential reasons for failure." To reduce the token count and ensure cost efficiency, which of the following prompt template modifications would best manage token usage while preserving essential information?

A) "Provide step-by-step instructions for troubleshooting the issue: [Issue Description]. Include commands and reasons for failure."
B) "Give troubleshooting instructions for [Issue Description], including steps, commands, and reasons for failure."
C) "Troubleshoot the following issue: [Issue Description]. Offer step-by-step commands and reasons for failure."
D) "Provide detailed troubleshooting instructions for the issue: [Issue Description], with steps, commands, and potential reasons for failure."

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

3. In the context of the decoding process for generative AI models in IBM Watsonx, what is the main characteristic of greedy decoding?

A) Greedy decoding selects the highest probability token at each step, leading to deterministic and often coherent outputs.
B) Greedy decoding always selects the token with the lowest probability to encourage diversity in the generated response.
C) Greedy decoding alternates between high and low probability tokens, ensuring a balance between creativity and correctness.
D) Greedy decoding generates multiple possible sequences and selects the most grammatically correct one based on predefined rules.

4. When designing a generative AI system to minimize the risk of producing hate speech or abusive content, which of the following strategies in prompt engineering is the most effective?

A) Use strict prompt filtering to block any input that contains offensive words.
B) Use offensive words in prompts to test the model's robustness in handling them.
C) Ensure that the model is trained on a larger dataset, even if the dataset contains diverse viewpoints, including some controversial opinions.
D) Implement real-time content moderation at the output level rather than relying on input prompts alone.

5. In a RAG system, you need to select an appropriate retriever to fetch relevant documents from a large corpus before generating an answer. You are considering different types of retrievers, including embedding-based and keyword-based retrievers.
Which of the following describes a scenario where an embedding-based retriever using a vector database is the best choice?

A) When exact keyword matching is required, and synonyms or contextual understanding are irrelevant
B) When most of the queries consist of structured queries with precise Boolean operators and relational database-style searches
C) When documents are labeled with metadata, and only metadata needs to be searched
D) When retrieval must rely on semantic similarity between a query and documents, even if the exact words in the query don't appear in the document

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