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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

A) Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
B) Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.
C) Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.
D) Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.

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

3. In the context of Generative AI, which of the following is an appropriate method for setting a stopping criterion to prevent a model from generating excessively long sequences?

A) Allowing the model to continue until it reaches the minimum token length specified
B) Setting a maximum token length for the output sequence
C) Stopping the model based on the number of unique tokens generated
D) Stopping the generation when the model reaches the end of its training dataset

4. You are tasked with creating a prompt template for generating environment descriptions in a generative AI model, which will be used for creating immersive virtual spaces.
Which of the following prompt best serves as a flexible template to generate diverse environment descriptions?

A) "Describe an environment where {mood} dominates, with {surroundings} contributing to the overall {atmosphere}. Include {time_of_day} and any other important details."
B) "Write about a dense jungle where wild animals roam freely, and the atmosphere is tense, full of suspense."
C) "Describe a futuristic city with towering skyscrapers and flying cars."
D) "Create a detailed description of a quiet forest during sunrise, focusing on the natural beauty of the trees, birds, and atmosphere."

5. In a RAG system, you need to select an appropriate retriever to fetch relevant documents from a large corpus before generating an answer. You are considering different types of retrievers, including embedding-based and keyword-based retrievers.
Which of the following describes a scenario where an embedding-based retriever using a vector database is the best choice?

A) When exact keyword matching is required, and synonyms or contextual understanding are irrelevant
B) When retrieval must rely on semantic similarity between a query and documents, even if the exact words in the query don't appear in the document
C) When documents are labeled with metadata, and only metadata needs to be searched
D) When most of the queries consist of structured queries with precise Boolean operators and relational database-style searches

Solutions: