C1000-185 EXAM GUIDES - C1000-185 TEST ANSWERS & C1000-185 EXAM TORRENT

C1000-185 Exam Guides - C1000-185 Test Answers & C1000-185 Exam Torrent

C1000-185 Exam Guides - C1000-185 Test Answers & C1000-185 Exam Torrent

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Tags: Test C1000-185 Question, New APP C1000-185 Simulations, C1000-185 Reliable Study Materials, C1000-185 Test Objectives Pdf, New C1000-185 Real Exam

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q217-Q222):

NEW QUESTION # 217
While working on generating a concise response to a user prompt, you notice that the generative AI model in IBM watsonx is producing excessively long outputs. You want to ensure that the response is informative but doesn't exceed a specific length.
Which of the following parameters should you adjust, and what is the most appropriate value to achieve a concise output without cutting off essential information?

  • A. Set max_tokens to 1000
  • B. Set max_tokens to 100
  • C. Set max_tokens to 10
  • D. Set max_tokens to 5

Answer: B


NEW QUESTION # 218
A financial institution is using a generative AI model to create reports based on transaction data. During deployment, the institution notices that the model sometimes fabricates trends or patterns that do not exist in the underlying data. This is an example of a hallucination.
Which of the following techniques would best minimize this risk during inference?

  • A. Reduce the model size to decrease its capacity to hallucinate complex patterns.
  • B. Use a retrieval-augmented generation (RAG) model that incorporates external financial data into the generation process.
  • C. Increase the top-p value to ensure more tokens are considered during generation.
  • D. Disable the model's autoregressive capability to prevent it from generating future predictions.

Answer: B


NEW QUESTION # 219
In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

  • A. Generating synthetic data that overrepresents the simplest task cases to reduce computational load
  • B. Avoiding any post-processing or filtering of synthetic data to retain diversity
  • C. Using domain-specific templates to generate synthetic data that reflects the target use case
  • D. Randomly generating sentences without adhering to the task-specific instructions

Answer: C


NEW QUESTION # 220
Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?

  • A. Quantization primarily reduces the size of the training dataset required for an LLM.
  • B. Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
  • C. Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.
  • D. Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.

Answer: B


NEW QUESTION # 221
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. 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.
  • C. Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
  • D. Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.

Answer: B


NEW QUESTION # 222
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