C1000-185 PREP EXAM & C1000-185 LATEST TORRENT & C1000-185 TRAINING GUIDE

C1000-185 Prep Exam & C1000-185 Latest Torrent & C1000-185 Training Guide

C1000-185 Prep Exam & C1000-185 Latest Torrent & C1000-185 Training Guide

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Tags: C1000-185 Test Lab Questions, C1000-185 Real Brain Dumps, C1000-185 Study Guide, Dumps C1000-185 Download, C1000-185 Test Torrent

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>> C1000-185 Test Lab Questions <<

Free PDF Quiz 2025 C1000-185: IBM watsonx Generative AI Engineer - Associate – High-quality Test Lab Questions

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q49-Q54):

NEW QUESTION # 49
In a Retrieval-Augmented Generation (RAG) system, you are tasked with generating vector embeddings for a large corpus of documents. You plan to use a pre-trained transformer-based model to generate these embeddings.
What is the most important factor to consider when choosing a pre-trained model for generating embeddings in this scenario?

  • A. The model should generate embeddings based on sentence-level inputs only, as document-level embeddings are always too large for effective retrieval.
  • B. The model should have been trained on a similar task (e.g., document retrieval), as this ensures the embeddings will be relevant for your corpus.
  • C. The model's size (in terms of parameters) should be minimized to reduce memory usage, even if it impacts embedding quality.
  • D. The model's embeddings should always be fine-tuned on your specific corpus before use, as pre-trained embeddings are too general for most tasks.

Answer: B


NEW QUESTION # 50
When tuning the generative model parameters, which of the following scenarios describes an appropriate use of the maximum tokens setting, and how will it influence the model's output?

  • A. Setting the maximum tokens to 0 will allow the model to generate an unlimited amount of text
  • B. Setting the maximum tokens to 100 will prevent the model from generating coherent sentences, as it cuts off abruptly after 100 tokens
  • C. Setting the maximum tokens to 100 ensures that the model's output will not exceed 100 tokens, but it may stop earlier if the generation ends naturally
  • D. Setting the maximum tokens to 500 will guarantee that the model always generates exactly 500 tokens

Answer: C


NEW QUESTION # 51
You are designing a prompt template for IBM Watsonx to generate various types of content for a company's internal communications. The goal is for the template to be reusable across different formats (emails, announcements, reports).
Which of the following best describes an effective template for this scenario?

  • A. "Produce a report with all necessary information."
  • B. "Create a [content type] for [audience], including key points on [subject]. Use a [tone] style and keep the length around [length]."
  • C. "Write the content for the communication based on the provided information."
  • D. ":Generate a formal communication piece for the company."

Answer: B


NEW QUESTION # 52
You have applied a set of prompt tuning parameters to a language model and collected the following statistics: ROUGE-L score, BLEU score, and memory utilization.
Based on these metrics, how would you prioritize further optimizations to balance the model's performance in terms of output relevance and resource efficiency?

  • A. Maximize BLEU score and reduce memory utilization
  • B. Reduce memory utilization and maintain BLEU and ROUGE-L scores
  • C. Increase memory utilization to reduce BLEU and ROUGE-L scores
  • D. Focus on improving the ROUGE-L score while increasing memory utilization

Answer: B


NEW QUESTION # 53
In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?

  • A. Embeddings are used to directly generate the textual responses in the output.
  • B. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
  • C. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.
  • D. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.

Answer: D


NEW QUESTION # 54
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