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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are optimizing a generative AI model that writes product descriptions. The cost of using the model is directly related to the number of tokens generated. To minimize token usage, you decide to introduce a stop sequence in your prompt that signals the model to end its generation early when the description reaches a certain length. Given the following prompt:
"Write a product description for [Product Name]. The description should include the main features and benefits of the product in no more than 50 words." Which of the following stop sequences would be most effective in ensuring the generation is concise and does not exceed the desired word limit?

A. "
B. ---"
C. ."
D. "
E. End of description."
F. "
G. "###END###"

Question 2

In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages.
Which of the following best describes when to use a vector database as part of the Retrieval-Augmented Generation (RAG) approach?

A. When there is a need to perform efficient keyword-based search on highly structured documents.
B. When the data consists of diverse unstructured documents, and you need to retrieve semantically similar content using dense vector representations.
C. When the dataset consists mainly of structured tabular data and relational queries.
D. When there is a requirement to process large volumes of streaming data in real-time, and exact matching is the priority.

Question 3

You are optimizing a generative AI prompt for creative content generation, but you want to ensure that outputs with the same prompt can vary slightly across multiple runs to maintain freshness in the responses.
Which parameter should you adjust to strike a balance between random variability and consistent quality?

A. Set a fixed Random Seed to zero
B. Temperature = 0.1, Random Seed unset
C. Top-p = 0.9, Temperature = 1.0, Random Seed unset
D. Max Tokens = 300, Random Seed set to 42

Question 4

In a scenario where a developer is creating reusable prompt templates for a Watsonx Generative AI project, what is the most effective method to track the usage and performance of these templates over time?

A. Leveraging Watsonx's native analytics and monitoring tools with built-in prompt tracking features
B. Embedding unique identifiers in the prompt templates and using Watsonx's logging mechanisms
C. Using static prompt templates without any tracking, as tracking adds unnecessary overhead
D. Relying on manual tracking of templates in a spreadsheet for each generation

Question 5

You are analyzing prompts submitted to a Generative AI model used for summarizing long research papers.
One user submits the following prompt: "Summarize this 40-page research paper on quantum computing, including details on every section and subsection, providing a detailed description of key points, methodologies, results, discussions, and future work. The summary should be at least 5 pages long." Why is this prompt considered inefficient, and how should it be optimized?

A. The prompt is inefficient because it does not specify a character limit, which means the model might generate overly verbose output.
B. The prompt is inefficient because it requests a 5-page summary, which is unnecessary for summarizing the key information from a research paper.
C. The prompt is efficient because it clearly outlines the expectations and ensures a comprehensive summary.
D. The prompt is inefficient because it asks for too much detail across all sections, leading to excessive token usage and unnecessary information in the output.

Solutions: