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C1000-185 New Braindumps Sheet - Verified C1000-185 Answers
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q90-Q95):
NEW QUESTION # 90
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 once it encounters a user query that requires it to reevaluate the entire prompt context and restart from the beginning.
- 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 as soon as the probability of the next token falls below the median probability of previously generated tokens.
Answer: C
NEW QUESTION # 91
IBM Watsonx Tuning Studio allows users to fine-tune pre-trained models for their specific use cases.
Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?
- A. It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.
- B. It enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
- C. It significantly reduces the computational costs associated with model fine-tuning by only updating the model's parameters relevant to the specific task, preserving the general knowledge of the base model.
- D. It fully retrains the base model from scratch, ensuring the highest possible accuracy for each new task, regardless of prior training.
Answer: C
NEW QUESTION # 92
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. 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.
- C. Test the new prompt in production without monitoring and observe user feedback to gauge performance.
- D. Replace the old prompt with the new one in the live system immediately to avoid confusion between prompt versions.
Answer: B
NEW QUESTION # 93
In the context of Generative AI (GenAI), various embedding models are used to represent textual data.
Which of the following best describes the difference between Word2Vec, BERT, and Sentence-BERT embedding models?
- A. Word2Vec creates static word embeddings, BERT generates dynamic embeddings based on context, and Sentence-BERT produces embeddings specifically optimized for sentence-level tasks like semantic similarity.
- B. Word2Vec captures contextual relationships between words, while BERT and Sentence-BERT generate sentence-level embeddings based on the overall document length.
- C. Word2Vec captures both word and sentence meanings in a single vector space, BERT generates only word embeddings, and Sentence-BERT generates embeddings for entire documents.
- D. Word2Vec uses a transformer architecture for embedding generation, whereas BERT and Sentence-BERT use neural networks to model context.
Answer: A
NEW QUESTION # 94
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?
- A. Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
- B. Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
- C. Fine-tuning the generative model on the entire document corpus without retrieval components.
- D. Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
Answer: B
NEW QUESTION # 95
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