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Valid C1000-185 Test Sample - C1000-185 Valid Test Bootcamp

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q122-Q127):

NEW QUESTION # 122
When debating the drawbacks of soft prompts in a generative AI application, which of the following is the most significant challenge compared to hard prompts?

A. Soft prompts significantly limit the flexibility of the model because they are tied to specific tasks, unlike hard prompts which can generalize to various scenarios.
B. Soft prompts require more human intervention during generation because they depend on predefined patterns and rules for guidance.
C. Soft prompts introduce more complexity during the training phase, as the model must learn embeddings that are not inherently interpretable by humans.
D. Soft prompts offer simpler debugging processes because the learned embeddings are directly linked to specific model behaviors and outputs.

Answer: C

NEW QUESTION # 123
You are implementing a Retrieval-Augmented Generation (RAG) system to enhance a large language model's (LLM) ability to answer questions based on an external document store.
What role do embeddings play in the RAG architecture, and how can you optimize them for more relevant document retrieval? (Select two)

A. Embeddings are only used during the generation phase of RAG to enhance language model outputs.
B. Embeddings are vector representations of text, used to measure the similarity between queries and documents.
C. Increasing the dimensionality of embeddings always improves retrieval accuracy by providing a more detailed representation of the documents.
D. Fine-tuning the embedding model with task-specific data can lead to more accurate retrievals.
E. The cosine similarity metric is typically less effective than Euclidean distance for comparing embeddings in RAG systems.

Answer: B,D

NEW QUESTION # 124
What is a key benefit of using Prompt Lab in IBM Watsonx to build reusable prompts for generative AI applications?

A. Prompt Lab uses predefined responses to train the model, eliminating the need for further prompt experimentation.
B. Prompt Lab automatically adjusts prompts based on the user's previous interactions, ensuring personalized output.
C. Prompt Lab allows for the creation of templates with dynamic placeholders, making it easier to reuse prompts in various contexts without modification.
D. Prompt Lab uses machine learning to automatically improve prompts over time without the need for developer intervention.

Answer: C

NEW QUESTION # 125
IBM Watsonx Tuning Studio offers several benefits when fine-tuning pre-trained models for specific tasks.
Which of the following is not a key benefit of using Tuning Studio?

A. Tuning Studio allows selective parameter updates, reducing the need to retrain the entire model for each new task.
B. Tuning Studio enables real-time, dynamic adjustments to the model's architecture during inference to handle new tasks.
C. Tuning Studio integrates with existing AI infrastructure to streamline model fine-tuning without requiring complex deployment processes.
D. Tuning Studio offers fine-tuning with minimal data, allowing users to adapt models to niche tasks without needing large datasets.

Answer: B

NEW QUESTION # 126
In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

A. Decreasing the number of tokens allowed in the output to encourage more creative responses.
B. Altering the tone of the prompt to make it more aligned with the brand's voice and customer expectations.
C. Increasing the temperature parameter to make the model more deterministic and consistent in its responses.
D. Modifying the stop sequence to ensure the model responds with shorter answers for concise customer interactions.

Answer: B

NEW QUESTION # 127
......

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