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[Hardware] New C1000-185 Mock Exam & Reliable C1000-185 Test Tutorial

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【Hardware】 New C1000-185 Mock Exam & Reliable C1000-185 Test Tutorial

Posted at yesterday 10:56      View：5 | Replies：0        Print      Only Author   [Copy Link] 1#
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q27-Q32):NEW QUESTION # 27
You are optimizing a Generative AI model for a business application where cost savings are a priority.
Which of the following modifications to the model's parameters will most effectively reduce the overall generation cost while minimizing the loss of output quality?
  • A. Set a lower value for the top-p (nucleus sampling) parameter.
  • B. Decrease the model's context window size.
  • C. Reduce the number of layers in the neural network during inference.
  • D. Set a lower value for the frequency penalty parameter.
Answer: A

NEW QUESTION # 28
You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?
  • A. Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.
  • B. Limiting the number of data points generated to avoid overfitting the synthetic data.
  • C. Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.
  • D. Using clustering techniques to group similar data points, preventing individual-level data from being exposed.
Answer: A

NEW QUESTION # 29
While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?
  • A. Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.
  • B. Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
  • C. Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.
  • D. Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
Answer: C

NEW QUESTION # 30
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. Directly integrate the external APIs with watsonx.ai without any intermediate framework, since LangChain would add unnecessary overhead.
  • B. Implement LangChain to handle complex multi-step workflows, using watsonx.ai's APIs to generate responses at specific stages in the chain.
  • C. Write custom scripts to manage all prompt sequences manually, leveraging watsonx.ai's SDK to call the generative AI model at every step.
  • D. Use only watsonx.ai's built-in APIs and SDKs for integration, as LangChain is not required for chaining multiple prompts.
Answer: B

NEW QUESTION # 31
You have just finished a prompt tuning experiment for a large language model (LLM) to optimize its output for generating customer support summaries. The tuning results show that while the accuracy of the generated summaries is high (95%), the response time for generating them has significantly increased. The experiment data suggests that increasing the maximum token length during tuning led to better quality summaries but with slower generation.
Which parameter should you adjust to improve the model's response time without sacrificing too much summary quality?
  • A. Change the decoding strategy from beam search to greedy decoding
  • B. Decrease the maximum token length
  • C. Decrease the number of epochs
  • D. Increase the batch size
Answer: A

NEW QUESTION # 32
......
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