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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision.
What does this likely indicate about the model's performance, and how should you approach further tuning?

A) The model's length of generated summaries is too short, indicating underfitting.
B) The model is overly conservative, missing relevant details; focus on improving recall.
C) The model is generating too many irrelevant details; focus on improving precision.
D) The model's summaries are incomplete, indicating poor understanding of the source material; consider fine-tuning the pre-trained embeddings.

2. You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?

A) Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed
B) Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.
C) Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
D) Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.

3. Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?

A) Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
B) Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
C) Prompt variables ensure that the same text input will always yield the same output, improving consistency.
D) Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.

4. A business is implementing a RAG solution to enhance its chatbot capabilities. The chatbot needs to answer queries using a large collection of unstructured documents.
Which scenario best highlights when to use a vector database to augment this system?

A) When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.
B) When performing traditional database operations like sorting and filtering based on numeric or categorical values.
C) When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.
D) When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.

5. As an IBM Watsonx Generative AI engineer, you are tasked with creating a chatbot for a public-facing service. One key concern is ensuring that the model does not generate or propagate hate speech, abusive content, or profanity. To mitigate these risks, you must implement appropriate controls.
Which of the following is the best approach to mitigate hate speech, abuse, and profanity from being generated by your AI model?

A) Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter to dynamically detect and block harmful content at inference time.
B) Apply a simple word-level blacklist filter to detect and remove harmful content from the model output.
C) Fine-tune the model with a highly curated dataset that contains labeled examples of hate speech, abuse, and profanity for the model to learn to avoid.
D) Train the model only on data that excludes all user-generated content to prevent exposure to harmful language.

Solutions: