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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are testing a new version of a prompt template designed to improve the accuracy of responses from a generative model deployed on IBM Watsonx. After deploying the new prompt version, you need to ensure that it performs better or at least as well as the previous version.
Which of the following approaches provides the most reliable method for testing the performance of the new prompt template version?

A) Use a random subset of production data and test both versions in a local environment, as local tests always replicate the conditions of production.
B) Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.
C) Run a series of A/B tests comparing the new prompt template to the old one, using a set of predetermined metrics, such as response accuracy and completion time.
D) Test the new prompt in production without monitoring and observe user feedback to gauge performance.

2. In the context of Retrieval-Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents.
Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?

A) Embeddings represent each word in a fixed-length vector based on its semantic meaning, which allows for precise retrieval of documents by directly matching words between the query and the documents.
B) Embeddings are primarily used to measure the grammatical correctness of the generated responses by checking the sentence structure in the generated text.
C) Embeddings are used to compress large documents into a smaller format, enabling faster storage and retrieval within a RAG system.
D) Embeddings are dense vector representations of words, phrases, or entire documents that capture semantic relationships, making it easier to find similar content in the knowledge base during retrieval.

3. You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A) Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
B) Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
C) Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.
D) Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
E) Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.

4. You are configuring a chatbot using IBM Watsonx, and you want the chatbot to respond appropriately based on the conversation's context.
Which of the following best represents an appropriate stopping criterion for a task where the chatbot generates step-by-step instructions?

A) The model will stop generating as soon as the probability of the next token falls below the median probability of previously generated tokens.
B) The model will stop generating once the instruction count reaches five steps, regardless of whether the task has been fully described.
C) The model will stop generating once it identifies a natural completion of the task description, such as reaching a final instruction or a conclusion marker (e.g., "All done").
D) The model will stop generating once it encounters a user query that requires it to reevaluate the entire prompt context and restart from the beginning.

5. In the context of a Retrieval-Augmented Generation (RAG) system using IBM Watsonx, which of the following is the correct process for generating vector embeddings for document retrieval?

A) Tokenize the text data, then pass the tokens through a pre-trained language model to generate vector embeddings for each token, combining them into a final vector.
B) Directly extract the vector embeddings from raw text without any tokenization or processing steps by passing the raw data to a pre-trained language model.
C) Use a pre-trained generative model to generate text predictions, then use these predicted tokens as the basis for vector embedding generation.
D) Load the text data into the model, tokenize it, and directly feed the tokens into a decoder for vector generation.

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