IBM C1000-185 Exam Questions : IBM watsonx Generative AI Engineer - Associate

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are implementing a Retrieval-Augmented Generation (RAG) system using LangChain, IBM WatsonX, and a vector database. The system needs to answer complex technical questions by retrieving relevant technical documents and generating a coherent response.
Which of the following best describes LangChain's role in the RAG pattern implementation?

A. LangChain serves as an intermediary, facilitating communication between the vector database and WatsonX's LLM, ensuring that retrieved documents are contextually relevant for the LLM's response generation.
B. LangChain is responsible for storing and retrieving documents using a sparse keyword-based retriever from a traditional relational database.
C. LangChain optimizes document retrieval by pre-generating responses based on common queries and caching them in memory for future use.
D. LangChain allows WatsonX's LLM to fine-tune its parameters based on user interactions to improve future retrieval and generation accuracy.

Question 2

You are designing a customer support chatbot using watsonx.ai as the primary generative model. You want to enhance the chatbot's capabilities by integrating it with IBM Watson Assistant to handle structured conversations while allowing watsonx.ai to generate responses for open-ended queries.
Which integration approach would most effectively combine both services while maintaining optimal performance and accuracy?

A. Use Watson Discovery to preprocess all queries before routing them to either Watson Assistant or watsonx.ai based on query complexity.
B. Create separate chat interfaces for Watson Assistant and watsonx.ai, and allow the user to choose which system to query based on their needs.
C. Train Watson Assistant to handle both structured and unstructured conversations, while using watsonx.ai only for rare edge cases that Watson Assistant cannot manage.
D. Implement Watson Assistant for structured conversations and use a middleware layer that dynamically routes complex, open-ended queries to watsonx.ai, returning the results within the same session.

Question 3

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

Question 4

You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A. Set the temperature to 0.0
B. Increase the temperature from 0.5 to 1.5
C. Decrease the temperature from 0.9 to 0.3
D. Decrease the temperature from 0.8 to 0.6

Question 5

You have just finished a prompt tuning experiment for a large language model (LLM) to optimize its output for generating customer support summaries. The tuning results show that while the accuracy of the generated summaries is high (95%), the response time for generating them has significantly increased. The experiment data suggests that increasing the maximum token length during tuning led to better quality summaries but with slower generation.
Which parameter should you adjust to improve the model's response time without sacrificing too much summary quality?

A. Decrease the maximum token length
B. Decrease the number of epochs
C. Increase the batch size
D. Change the decoding strategy from beam search to greedy decoding

Solutions: