VALID TEST C1000-185 FORMAT & CUSTOMIZED C1000-185 LAB SIMULATION

Valid Test C1000-185 Format & Customized C1000-185 Lab Simulation

Valid Test C1000-185 Format & Customized C1000-185 Lab Simulation

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q61-Q66):

NEW QUESTION # 61
When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?

  • A. Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
  • B. Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.
  • C. Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.
  • D. Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.

Answer: D


NEW QUESTION # 62
You are tasked with improving the performance of a Retrieval-Augmented Generation (RAG) system in IBM watsonx. Part of this improvement involves selecting the right embedding model for document retrieval.
Which of the following is the best description of the differences between various embedding models, and how would you choose the most suitable model for your task?

  • A. Word2Vec embeddings capture only the syntactic relationships between words, while BERT embeddings focus on both syntax and semantic context, making BERT more suitable for complex retrieval tasks in a RAG system.
  • B. TF-IDF is an advanced embedding model that captures both the frequency and semantic meaning of words, making it more effective than deep learning-based models like BERT for retrieval in RAG systems.
  • C. Word2Vec, GloVe, and BERT are all embedding models, but BERT embeddings capture richer context by considering the entire sentence rather than just the local context, making it more effective for generating semantically relevant embeddings.
  • D. BERT embeddings are context-independent, which makes them less useful for a RAG system than Word2Vec or GloVe, which focus on learning semantic relationships between words.

Answer: C


NEW QUESTION # 63
You are working on a project that involves deploying a series of prompt templates for a large language model on the IBM Watsonx platform. The team has requested a system that supports prompt versioning so that updates to the prompts can be tracked and tested over time.
Which of the following is the most important consideration when planning prompt versioning for deployment?

  • A. The versioning system should automatically downgrade to the previous prompt version if the model returns a confidence score below a certain threshold during inference.
  • B. Version control should focus exclusively on the syntactical structure of the prompts, as changes to prompt content rarely impact the model's performance.
  • C. Each version of the prompt must have a unique identifier that can be referenced during model inference, to avoid conflicting results from different prompt versions.
  • D. Prompts should be stored in a proprietary IBM format, as other formats are not compatible with the Watsonx platform when using versioning.

Answer: C


NEW QUESTION # 64
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 enables on-the-fly model optimization during inference, adjusting model weights dynamically based on real-time data input.
  • B. It allows users to add new architectural layers to the model to improve accuracy without retraining the entire model.
  • 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 # 65
Which of the following represents the most effective use of example input prompts within IBM Watsonx's Prompt Lab for generating a high-quality response?

  • A. Crafting prompts with very specific and detailed instructions, ensuring the model follows a strict framework for the desired response.
  • B. Using example prompts that introduce multiple topics at once to evaluate how well the model handles multitasking during response generation.
  • C. Writing prompts that are extremely vague, allowing the model to freely interpret the input and generate diverse responses.
  • D. Using prompts with complex sentence structures and advanced terminology to challenge the model's understanding capabilities.

Answer: A


NEW QUESTION # 66
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