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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When optimizing the tuning process in IBM Watsonx Tuning Studio for a Generative AI model, which approach would best reduce training time and computational cost while maintaining model performance?

A) Increase the batch size and reduce the learning rate simultaneously to speed up the tuning process and minimize training iterations.
B) Focus the tuning on adjusting only the model's last few layers, which are responsible for task-specific outputs, while leaving the majority of the model unchanged.
C) Use all available training data, including unrelated examples, to ensure the model has a broad understanding of multiple tasks before tuning.
D) Perform full-scale retraining of the model for each new task to ensure maximum adaptability and accuracy.

2. You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A) "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
B) "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."
C) "Example 1: [First Contract]
D) "Generate a summary for [New Contract]."

3. In the context of the decoding process for generative AI models in IBM Watsonx, what is the main characteristic of greedy decoding?

A) Greedy decoding generates multiple possible sequences and selects the most grammatically correct one based on predefined rules.
B) Greedy decoding alternates between high and low probability tokens, ensuring a balance between creativity and correctness.
C) Greedy decoding always selects the token with the lowest probability to encourage diversity in the generated response.
D) Greedy decoding selects the highest probability token at each step, leading to deterministic and often coherent outputs.

4. You are tasked with designing a generative AI model to assist users in filling out a form that collects personal information, such as email addresses and phone numbers.
What is the most appropriate method to ensure the model can differentiate between required personal information and unnecessary sensitive data that should not be included in the output?

A) Train the model exclusively on datasets that contain no personal information
B) Embed privacy-sensitive heuristics in the model's prompt to guide its behavior
C) Use a post-generation filter to remove any text that appears to be personal information
D) Leverage regular expressions to filter out personal information in the prompt

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 efficient because it clearly outlines the expectations and ensures a comprehensive summary.
B) 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.
C) The prompt is inefficient because it requests a 5-page summary, which is unnecessary for summarizing the key information from a research paper.
D) The prompt is inefficient because it does not specify a character limit, which means the model might generate overly verbose output.

Solutions: