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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?

A) Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.
B) Limiting the number of data points generated to avoid overfitting the synthetic data.
C) Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.
D) Using clustering techniques to group similar data points, preventing individual-level data from being exposed.

2. You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.
Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

A) Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
B) Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.
C) Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.
D) Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.

3. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.
B) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
C) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
D) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.

4. You are tuning a generative AI model to reduce repetitive outputs, which often occur when generating long texts.
Which of the following parameter adjustments would most likely reduce the model's tendency to repeat words or phrases without compromising the quality of the generated text?

A) Lowering the temperature to 0.1
B) Reducing the beam size to 1
C) Increasing the Top-k value to 200
D) Increasing the repetition penalty to 1.2

5. Your organization is deploying a generative AI model to assist in legal document generation. During testing, you discover that the model generates biased legal advice that could disproportionately affect certain social groups. Additionally, a team member raises concerns about potential data poisoning attacks on your training set.
What steps should you take to mitigate both the risks of data bias and poisoning?

A) Use adversarial training techniques to make the model more robust against bias and poisoning.
B) Implement a data validation pipeline to detect anomalies and potential poisoning in the training data.
C) Fine-tune the model using only the training data from trusted sources, without expanding the dataset.
D) Apply prompt engineering techniques to avoid triggering known biases in the model.

Solutions: