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Brilliantly Updated IBM C1000-185 Exam Dumps

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q266-Q271):

NEW QUESTION # 266
After prompt-tuning a language model, you notice that certain outputs are semantically correct but syntactically flawed.
Which of the following actions is most appropriate to resolve this issue and optimize the tuned model's performance?

A. Lower the learning rate during the tuning phase
B. Fine-tune the prompt template to emphasize grammar
C. Use a higher temperature during the generation process
D. Increase the model's training dataset size

Answer: B

NEW QUESTION # 267
When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A. Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
B. Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
C. Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.
D. Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.

Answer: C

NEW QUESTION # 268
You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

A. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
B. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
C. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
D. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.

Answer: B

NEW QUESTION # 269
You are implementing a RAG system that uses vector embeddings to retrieve relevant information from a large corpus of documents. To store and efficiently query the vector embeddings, you need to choose a suitable vector database.
Which of the following is a key capability that a vector database must support to ensure efficient retrieval in this context?

A. Automatic generation of vector embeddings from raw text data
B. The ability to store sparse vectors instead of dense vectors
C. The ability to store and query data only in JSON format
D. Built-in support for Approximate Nearest Neighbor (ANN) search algorithms

Answer: D

NEW QUESTION # 270
When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?

A. Using temperature control during generation to manage diversity in responses
B. Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
C. Training the model on a diverse and representative dataset
D. Incorporating fairness constraints during the model's training phase

Answer: A

NEW QUESTION # 271
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