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[General] Pass Guaranteed 2026 C1000-185: IBM watsonx Generative AI Engineer - Associate H

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【General】 Pass Guaranteed 2026 C1000-185: IBM watsonx Generative AI Engineer - Associate H

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q37-Q42):NEW QUESTION # 37
Which two of the following are key benefits of using IBM Watsonx's Tuning Studio for fine-tuning generative AI models on specific tasks? (Select two)
  • A. Offers real-time validation of tuning experiments to optimize model performance
  • B. Automatically generates prompt templates based on data input
  • C. Significantly reduces the risk of overfitting without manual intervention
  • D. Automatically selects the best model architecture for the task
  • E. Provides automated hyperparameter search and tuning strategies
Answer: A,E

NEW QUESTION # 38
You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?
  • A. Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.
  • B. Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.
  • C. Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.
  • D. Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.
Answer: C

NEW QUESTION # 39
Which of the following represents the most effective use of example input prompts within IBM Watsonx's Prompt Lab for generating a high-quality response?
  • A. Crafting prompts with very specific and detailed instructions, ensuring the model follows a strict framework for the desired response.
  • B. Using example prompts that introduce multiple topics at once to evaluate how well the model handles multitasking during response generation.
  • C. Using prompts with complex sentence structures and advanced terminology to challenge the model's understanding capabilities.
  • D. Writing prompts that are extremely vague, allowing the model to freely interpret the input and generate diverse responses.
Answer: A

NEW QUESTION # 40
You are tasked with building a Retrieval-Augmented Generation (RAG) system using Elasticsearch for document storage, Watson ML for model hosting, and LangChain for orchestration. The chatbot is supposed to query a large database of medical records and generate responses based on the retrieved information. During testing, you notice that irrelevant documents are often retrieved, leading to low-quality responses.
What would be the best approach to improve document relevance in this RAG setup?
  • A. Implement a BM25 similarity algorithm in Elasticsearch to improve document retrieval.
  • B. Pre-process the medical records in LangChain to remove irrelevant information before sending the query to Elasticsearch.
  • C. Update the Elasticsearch index by re-indexing documents using embeddings from the Watson ML model.
  • D. Fine-tune the Watson ML model on a dataset containing both queries and relevant documents, then update Elasticsearch with the model outputs.
Answer: C

NEW QUESTION # 41
You are using IBM Watsonx to control the randomness of a language model's output by adjusting the top-k parameter.
What happens when you reduce the top-k value from 50 to 5 during text generation?
  • A. Reducing the top-k value reduces the model's ability to predict rare or uncommon words, leading to less accurate outputs.
  • B. Reducing the top-k value increases the temperature of the model, making the outputs more creative and diverse.
  • C. Lowering the top-k value forces the model to generate shorter outputs by limiting the number of tokens available for selection.
  • D. The model will now consider only the top 5 most probable tokens at each step, making the output more deterministic and focused.
Answer: D

NEW QUESTION # 42
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