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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
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 pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.

Answer: A

NEW QUESTION # 26
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 pre-trained generative models that augment the retrieval step by generating new query variations.
B. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
C. Embeddings are used to directly generate the textual responses in the output.
D. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.

Answer: B

NEW QUESTION # 27
You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user's request.
How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?

A. Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user's request.
B. Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.
C. Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.
D. Rely on LangChain's memory module to remember previous user queries and provide real-time data access.

Answer: A

NEW QUESTION # 28
You are using IBM watsonx's generative AI model to generate responses for a chatbot, and you want to ensure that the model stops generating text when it encounters a specific phrase like ":End of Response." Which of the following settings for stop sequences is most appropriate to achieve this goal?

A. Set the stop sequence to "\ n\ n"
B. Set the stop sequence to "STOP"
C. Set the stop sequence to "End of Response"
D. Set the stop sequence to "<|stop|>"

Answer: C

NEW QUESTION # 29
You are tasked with optimizing a generative AI model in IBM watsonx.ai for an NLP-based application.
During the planning stage, which of the following data elements is most important to ensure the model generalizes well to real-world application usage?

A. Only the structured data relevant to specific business rules
B. High-dimensional feature vectors from a small, well-curated dataset
C. Pre-processed data with stop words removed to reduce noise
D. A large, diverse dataset with both structured and unstructured data

Answer: D

NEW QUESTION # 30
......

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Free PDF IBM - C1000-185 - Newest IBM watsonx Generative AI Engineer - Associate Exam Simulator Online

New C1000-185 Exam Answers | C1000-185 Valid Exam Syllabus

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q25-Q30):

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