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[General] C1000-185 Valid Test Practice | C1000-185 Exam Format

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【General】 C1000-185 Valid Test Practice | C1000-185 Exam Format

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q324-Q329):NEW QUESTION # 324
What is a key benefit of using Prompt Lab in IBM Watsonx to build reusable prompts for generative AI applications?
  • A. Prompt Lab uses machine learning to automatically improve prompts over time without the need for developer intervention.
  • B. Prompt Lab automatically adjusts prompts based on the user's previous interactions, ensuring personalized output.
  • C. Prompt Lab allows for the creation of templates with dynamic placeholders, making it easier to reuse prompts in various contexts without modification.
  • D. Prompt Lab uses predefined responses to train the model, eliminating the need for further prompt experimentation.
Answer: C

NEW QUESTION # 325
A team is fine-tuning a large language model (LLM) for a healthcare application. They have decided to implement a taxonomy tree-based curation to prepare their dataset of medical records and patient interactions.
What is the primary benefit of using a taxonomy tree in the curation process for such a model?
  • A. It reduces the overall size of the dataset by filtering out irrelevant data.
  • B. It eliminates the need for data cleaning and preprocessing since the taxonomy provides the structure.
  • C. It ensures that the model only focuses on specific medical specialties by eliminating other categories.
  • D. It provides a hierarchical structure that helps the model understand the relationships between different medical concepts.
Answer: D

NEW QUESTION # 326
You are part of a team building an AI-powered assistant that helps software developers by answering technical queries. To handle the vast amount of technical documentation efficiently, the team has chosen to implement the Retrieval-Augmented Generation (RAG) pattern. LangChain is used to construct the retrieval-generation pipeline, and SingleStore is employed to store the document embeddings. You need to ensure that the integration of LangChain with SingleStore allows for real-time retrieval of relevant technical documentation.
Which of the following configurations should be used to ensure an optimal RAG implementation with LangChain and SingleStore?
  • A. Use LangChain to embed documents into a vector space and store these embeddings in SingleStore. During inference, embed the query in the same vector space and retrieve documents using vector similarity search.
  • B. Use LangChain to retrieve pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.
  • C. Use LangChain to embed both queries and documents into the same vector space and store these embeddings in SingleStore. During inference, retrieve documents using SQL queries based on keyword similarity.
  • D. Use LangChain to retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
Answer: A

NEW QUESTION # 327
You are generating a list of items using IBM watsonx's generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence.
What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?
  • A. Increase the token limit to avoid premature cut-off
  • B. Use multiple stop sequences, including a period ."
  • C. Set the stop sequence to a punctuation mark like ";"
  • D. Use a more distinct and unlikely stop sequence, such as "<END>"
Answer: D

NEW QUESTION # 328
In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?
  • A. Randomly selecting tokens from the top-k highest probability tokens at each step
  • B. Averaging the probabilities of multiple tokens at each step to generate a sequence
  • C. Selecting the token with the highest probability at each step, without considering future possibilities
  • D. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
Answer: C

NEW QUESTION # 329
......
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