RELIABLE IBM C1000-185 DUMPS BOOK | C1000-185 VALID TEST QUESTIONS

Reliable IBM C1000-185 Dumps Book | C1000-185 Valid Test Questions

Reliable IBM C1000-185 Dumps Book | C1000-185 Valid Test Questions

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q118-Q123):

NEW QUESTION # 118
When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

  • A. It allows incremental training, saving computational resources by reusing checkpoints.
  • B. It automatically increases the number of layers for more complex tasks.
  • C. It expands the model's architecture to handle larger datasets.
  • D. It optimizes the model for multilingual capabilities by adding new embeddings.

Answer: A


NEW QUESTION # 119
You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

  • A. Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
  • B. Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
  • C. Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
  • D. Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
  • E. Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.

Answer: A,B


NEW QUESTION # 120
You are tasked with designing a LangChain-based AI workflow using watsonx.ai that incorporates multiple models for different tasks: document classification, entity extraction, and text generation. The final output should consist of a well-structured report that combines these processes.
What is the best strategy to orchestrate this workflow to ensure seamless integration of all tasks and a coherent final output?

  • A. Build a modular workflow where each task operates independently without passing data between them, ensuring each task can be validated separately before generating the final report.
  • B. Start with text generation using watsonx.ai and then refine the output through document classification and entity extraction.
  • C. Run the document classification, entity extraction, and text generation tasks in parallel and merge the results at the end to produce the final report.
  • D. Use document classification to filter the input data, followed by entity extraction to identify key terms, and then pass the refined information to watsonx.ai for text generation.

Answer: D


NEW QUESTION # 121
You are tasked with configuring a generative model to assist legal professionals by generating contract clauses. The model should produce complete, contextually relevant clauses but should avoid overly long and redundant text. You must ensure that the generation process stops appropriately once a clause is completed.
Which of the following strategies is the most appropriate for configuring stopping criteria?

  • A. Set the top-k value to 1 and use the default maximum token limit, relying on the model's internal stopping criteria.
  • B. Use a beam search algorithm and impose a strict length restriction of 100 tokens.
  • C. Use a stop sequence based on legal clause delimiters and impose a minimum token limit of 50.
  • D. Set a maximum token limit of 500 and implement a fixed length stopping criterion.

Answer: C


NEW QUESTION # 122
You are using IBM's Tuning Studio to fine-tune a generative AI model for a custom text classification task. The model was pre-trained on a large corpus but shows suboptimal performance when applied to your domain-specific data. You aim to improve both accuracy and computational efficiency.
Which of the following is a primary benefit of using Tuning Studio to optimize this model?

  • A. Tuning Studio allows for the customization of training data at runtime without needing pre-processing.
  • B. Tuning Studio provides detailed performance analytics that allow you to adjust hyperparameters in real-time.
  • C. Tuning Studio automatically generates prompt templates that can be used for different tasks without further configuration.
  • D. Tuning Studio helps reduce overfitting by applying regularization techniques during the fine-tuning process.

Answer: B


NEW QUESTION # 123
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