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C1000-185 valid cram guide & C1000-185 training prep & C1000-185 sure pass
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q27-Q32):
NEW QUESTION # 27
You are managing a generative AI model deployment in IBM Watsonx and need to implement prompt versioning to ensure traceability and reproducibility of model behavior over time.
Which of the following strategies best enables versioning of prompts during deployment?
- A. Relying on model checkpointing to manage both model weights and prompts.
- B. Disabling versioning for prompts since it is not required for generative models.
- C. Storing prompts in a flat file system and manually tracking versions.
- D. Using a source control system (e.g., Git) to track prompt changes alongside model code.
Answer: D
NEW QUESTION # 28
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 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.
- 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 retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
- D. 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.
Answer: D
NEW QUESTION # 29
You are working on deploying a generative AI model into production. The goal is to ensure that different versions of prompts can be tracked and rolled back in case of degradation in the model's performance.
Which of the following strategies would best address versioning for deployment?
- A. Use manual version control through logging and local storage.
- B. Integrate prompt versioning into the model deployment pipeline using automated versioning tools like Git.
- C. Use endpoint monitoring tools only, without any versioning approach, to track and assess prompt changes in production.
- D. Maintain different versions of the model and prompts by duplicating them across multiple endpoints without a centralized repository.
Answer: B
NEW QUESTION # 30
You are generating product descriptions for an online marketplace using a generative AI model. The output is coherent but tends to repeat the same phrases and words excessively. You decide to apply a repetition penalty to reduce this repetition while keeping the temperature set to a value that maintains creativity in the text generation.
Which of the following adjustments would best achieve this goal?
- A. Set repetition penalty to 1.5 and maintain temperature at 0.8
- B. Set repetition penalty to 0.0
- C. Set repetition penalty to 2.0 and increase temperature from 0.7 to 1.2
- D. Set repetition penalty to 1.0 and decrease temperature from 0.8 to 0.3
Answer: A
NEW QUESTION # 31
In developing an LLM-based conversational AI application using LangChain, you want the AI to perform complex tasks, such as answering questions based on dynamic knowledge from multiple sources (e.g., databases, APIs, etc.).
Which approach using LangChain best supports this requirement by combining various tools into a structured workflow for the AI to follow?
- A. Build a chain of multiple LangChain agents, each handling a specific task (e.g., querying an API, accessing a database) to ensure data from various sources is used effectively.
- B. Integrate LangChain memory with an agent to handle all external data retrieval without needing to build complex chains.
- C. Create a LangChain chain that connects different tools (e.g., API access, database queries) in a sequential or branching manner to process and combine data dynamically.
- D. Use a single LangChain agent to directly query all external data sources, allowing it to gather information on demand.
Answer: C
NEW QUESTION # 32
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