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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Your team is tasked with enhancing a document search engine using IBM watsonx Discovery. The search engine will be used to help employees quickly find relevant internal documentation, such as policy guides, project specifications, and technical manuals. You have decided to use Retrieval-Augmented Generation (RAG) to enhance this search engine's performance. The current focus is on selecting the best retriever for the task and setting up a vector database to store document embeddings.
Which of the following retriever types is most suitable for this RAG-based search engine setup, considering the diverse and unstructured nature of the documents, and why?

A) Rule-based retriever using manually crafted rules to find documents based on predefined logic.
B) Sparse retriever with BM25 ranking in combination with a traditional relational database.
C) Hybrid retriever that combines sparse retrieval methods with dense embeddings stored in a flat-file system.
D) Sparse retriever with BM25 ranking in combination with a traditional relational database.

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 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.
B) 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
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 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.

3. In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

A) Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
B) Increase the model's randomness by adjusting the temperature parameter
C) Reduce the model's token limit to restrict the amount of text generated
D) Introduce explicit instructions in the prompt to avoid generating personal information

4. When setting up a tuning experiment in IBM watsonx's Tuning Studio, which of the following best describes the process for optimizing a model's hyperparameters?

A) Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.
B) All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.
C) Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.
D) Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.

5. In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A) It reduces the time required to train models by lowering the accuracy threshold.
B) It provides real-time feedback on model deployment success rates.
C) It limits the number of model versions stored, improving system performance.
D) It allows for the precise allocation of computational resources to manage budget constraints.

Solutions: