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2025 100% Free C1000-185–Valid 100% Free Books PDF | Exam C1000-185 Questions
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q184-Q189):
NEW QUESTION # 184
In the context of Generative AI, which of the following is an appropriate method for setting a stopping criterion to prevent a model from generating excessively long sequences?
- A. Stopping the model based on the number of unique tokens generated
- B. Setting a maximum token length for the output sequence
- C. Allowing the model to continue until it reaches the minimum token length specified
- D. Stopping the generation when the model reaches the end of its training dataset
Answer: B
NEW QUESTION # 185
How can developers leverage Prompt Lab in IBM Watsonx to build reusable prompts for a conversational AI system that handles multiple topics?
- A. By generating a unique prompt for each possible user query to ensure maximum precision.
- B. By creating a single prompt template that adapts to all user inputs regardless of the subject.
- C. By utilizing Prompt Lab's automatic suggestion feature, which builds new prompts based on the system's training data.
- D. By designing prompts with dynamic variables for user-specific information, allowing for flexibility across different topics.
Answer: D
NEW QUESTION # 186
You are tasked with integrating a generative AI model on watsonx.ai into a custom business workflow. The workflow requires complex prompt chains and interaction with external APIs.
Which of the following best describes how you should approach the integration using watsonx.ai and LangChain?
- A. Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.
- B. Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.
- C. Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.
- D. Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.
Answer: A
NEW QUESTION # 187
You are working on generating creative text responses using IBM watsonx's generative AI model. You need to adjust the output so that it is more diverse and creative without losing coherence.
Which of the following model parameter settings would best achieve this objective?
- A. Set temperature to 0.2
- B. Set temperature to 1.5
- C. Set temperature to 2
- D. Set temperature to 0
Answer: B
NEW QUESTION # 188
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. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.
- B. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.
- C. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
- D. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
Answer: A
NEW QUESTION # 189
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