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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q87-Q92):

NEW QUESTION # 87
Your company is working on deploying a Watsonx Generative AI model for a client, and you have been asked to define the roles involved in the deployment process.
Which of the following roles is responsible for ensuring that the model is properly integrated into the client's existing systems and that data pipelines are established for continuous model improvement?

A. Solution Architect
B. Data Engineer
C. DevOps Engineer
D. Data Scientist

Answer: A

NEW QUESTION # 88
You are refining a prompt for an AI model that generates financial reports using IBM watsonx Prompt Lab.
To ensure the AI produces structured outputs such as sections on "Revenue," "Expenses," and "Net Profit," which of the following is the most effective prompt editing option to achieve this goal?

A. Use static prompt text and avoid variables to maintain a consistent structure for each report.
B. Incorporate specific section headers as part of the prompt to guide the model in generating the desired report format.
C. Adjust the temperature setting to generate more creative and detailed reports.
D. Increase the randomness parameter to encourage diverse sections in the report structure.

Answer: B

NEW QUESTION # 89
Your team is building a natural language processing pipeline using IBM watsonx components, where data from multiple external APIs and user inputs needs to be transformed, analyzed, and routed through various AI models. The process should involve the dynamic selection of models based on input data characteristics. The goal is to minimize latency while maintaining accuracy across tasks like sentiment analysis, text summarization, and query generation.
Which IBM watsonx service would you use to implement a flexible, model orchestration pipeline that meets these requirements, and why?

A. IBM watsonx Data Refinery, as it can preprocess and analyze incoming data, and use its rules- based engine to route it to different models.
B. IBM watsonx Orchestrator, which allows for the integration and management of multiple AI models and can dynamically route inputs to the appropriate model based on predefined criteria.
C. IBM watsonx API Gateway to handle external data inputs, route them to different models, and ensure that each input is preprocessed in a low-latency manner.
D. IBM watsonx Model Management to dynamically select and orchestrate the models for different tasks based on real-time data analysis.

Answer: B

NEW QUESTION # 90
You are implementing a Retrieval-Augmented Generation (RAG) system to enhance a large language model's (LLM) ability to answer questions based on an external document store.
What role do embeddings play in the RAG architecture, and how can you optimize them for more relevant document retrieval? (Select two)

A. Fine-tuning the embedding model with task-specific data can lead to more accurate retrievals.
B. Increasing the dimensionality of embeddings always improves retrieval accuracy by providing a more detailed representation of the documents.
C. Embeddings are only used during the generation phase of RAG to enhance language model outputs.
D. The cosine similarity metric is typically less effective than Euclidean distance for comparing embeddings in RAG systems.
E. Embeddings are vector representations of text, used to measure the similarity between queries and documents.

Answer: A,E

NEW QUESTION # 91
You are using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

A. Use prompt engineering to provide more explicit instructions for the model
B. Increase the number of fine-tuning epochs to ensure the model converges
C. Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data
D. Adjust the token length limit in the model's configuration

Answer: A

NEW QUESTION # 92
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C1000-185 Reliable Dumps Free, New C1000-185 Exam Papers

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q87-Q92):

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