HIGH PASS-RATE C1000-185 TEST QUIZ - PASS C1000-185 ONCE - FANTASTIC C1000-185 RELIABLE TEST EXPERIENCE

High Pass-Rate C1000-185 Test Quiz - Pass C1000-185 Once - Fantastic C1000-185 Reliable Test Experience

High Pass-Rate C1000-185 Test Quiz - Pass C1000-185 Once - Fantastic C1000-185 Reliable Test Experience

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Undoubtedly, passing the IBM C1000-185 certification exam is one big achievement. Regardless of how tough the C1000-185 exam is, it serves an important purpose of improving your skills and knowledge of a specific field. Once you become certified by IBM C1000-185, a whole new career scope will open up to you.

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q342-Q347):

NEW QUESTION # 342
You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

  • A. BLEU score
  • B. Perplexity score
  • C. Log-likelihood of generated responses
  • D. Token generation speed

Answer: B


NEW QUESTION # 343
After tuning a generative AI model to produce more concise legal document summaries, you notice that while the summaries are accurate, they tend to be overly verbose. The tuning report shows that the model's perplexity is relatively high, suggesting that it is struggling with token prediction uncertainty, possibly due to an overly complex output format.
Which of the following tuning parameters would you most likely adjust to address the verbosity issue without reducing accuracy?

  • A. Increase the number of epochs
  • B. Increase the maximum token length
  • C. Decrease the learning rate
  • D. Decrease the temperature

Answer: D


NEW QUESTION # 344
You are designing a generative AI model to generate detailed technical

  • A. Stop Sequence = 'END', Max Tokens = 500
  • B. Max Tokens = 500: This provides enough space for a detailed technical
  • C. Stop Sequence = None, Max Tokens = 700
  • D. Stop Sequence = '<END>', Max Tokens = 100

Answer: A


NEW QUESTION # 345
A business is implementing a RAG solution to enhance its chatbot capabilities. The chatbot needs to answer queries using a large collection of unstructured documents.
Which scenario best highlights when to use a vector database to augment this system?

  • A. When working with large amounts of unstructured text data, to enable semantic search through embeddings that represent the meaning of documents.
  • B. When storing highly structured relational data, as a vector database excels at managing tabular information efficiently.
  • C. When you need to provide answers based on the keywords present in the documents, as vector databases are designed for keyword-based retrieval.
  • D. When performing traditional database operations like sorting and filtering based on numeric or categorical values.

Answer: A


NEW QUESTION # 346
In planning the deployment of a generative AI model that relies on a large corpus of data, you need to organize and version the data repository used for training prompts.
Which of the following approaches best ensures efficient data versioning, integrity, and easy rollback during prompt refinement?

  • A. Use a relational database to store the corpus, but without tracking any changes to the datasets.
  • B. Store all datasets in a monolithic repository and append new data versions as additional files with timestamps.
  • C. Store data versions directly in the production environment without versioning to save on storage costs.
  • D. Implement a data versioning system like DVC (Data Version Control) or MLflow, integrated with a cloud storage service, to track data changes and link specific data versions to corresponding model and prompt versions.

Answer: D


NEW QUESTION # 347
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