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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Your team is tasked with enhancing a document search engine using IBM watsonx Discovery. The search engine will be used to help employees quickly find relevant internal documentation, such as policy guides, project specifications, and technical manuals. You have decided to use Retrieval-Augmented Generation (RAG) to enhance this search engine's performance. The current focus is on selecting the best retriever for the task and setting up a vector database to store document embeddings.
Which of the following retriever types is most suitable for this RAG-based search engine setup, considering the diverse and unstructured nature of the documents, and why?

A) Rule-based retriever using manually crafted rules to find documents based on predefined logic.
B) Sparse retriever with BM25 ranking in combination with a traditional relational database.
C) Hybrid retriever that combines sparse retrieval methods with dense embeddings stored in a flat-file system.
D) Sparse retriever with BM25 ranking in combination with a traditional relational database.

2. You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?

A) Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
B) Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.
C) Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.
D) Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed

3. You are developing a solution that requires generating various reports using IBM Watsonx. To maintain efficiency and avoid redundant prompt creation, you decide to implement reusable prompts.
Which of the following best summarizes the value of using reusable prompts in this context?

A) Reusable prompts ensure that the AI model continues to evolve without additional training, as the prompts themselves become more refined over time.
B) Reusable prompts eliminate the need for monitoring model performance since they consistently deliver accurate results.
C) Reusable prompts automatically adjust their structure depending on the input, eliminating the need for dynamic parameters or customization.
D) They allow for faster deployment of new AI-driven solutions since the same prompt templates can be adapted for multiple tasks with minimal changes.

4. You are tasked with designing a prompt to fine-tune an IBM Watsonx model to summarize legal documents. The summaries must include only factual information, highlight key legal terms, and exclude any personal interpretations or subjective analysis.
Which of the following is the best prompt to achieve this goal?

A) "Generate a detailed and engaging summary of this legal document, adding your insights to clarify complex legal points for the reader."
B) "Provide a summary of this legal document, focusing on factual information, including key legal terms and avoiding personal interpretation or subjective analysis."
C) "Create a brief summary of this legal document, ensuring to exclude any legal jargon and simplifying the content for a layperson audience."
D) "Summarize this legal document, focusing on key arguments and providing an analysis of the potential outcomes of the case."

5. You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

A) Reducing the token limit in the model configuration to restrict the length of responses.
B) Using temperature scaling to increase randomness and reduce token usage.
C) Revising the prompt to make it more specific by narrowing the scope of expected responses.
D) Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.

Solutions: