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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. Which of the following techniques can be most effectively used to mitigate the generation of hate speech, abuse, and profanity in generative AI models when applying prompt engineering?

A) Applying Token Regularization to limit the diversity of generated responses
B) Using Greedy Decoding to ensure that the model outputs the most likely sequence of tokens
C) Fine-tuning the model with specific datasets curated to exclude offensive content
D) Restricting the model's ability to generate certain words or phrases using stop-word lists

2. You are creating a prompt template for a generative AI model that helps technical support staff troubleshoot customer issues based on symptoms provided. The goal is to generate a clear, step-by-step diagnostic process.
What elements would improve the effectiveness of the template? (Select two)

A) Provide explicit instructions to break the response into steps
B) Include an instruction to ask clarifying questions when the issue is unclear
C) Incorporate complex technical jargon to ensure expert-level responses
D) Set a high temperature value to explore more creative diagnostic approaches
E) Ask the model to generate multiple diagnostic paths for each issue

3. You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface.
Which of the following steps should you follow to ensure the model is fine-tuned correctly and the synthetic data is used effectively?

A) Directly upload synthetic data without inspecting or validating it and initiate the fine-tuning process.
B) Use synthetic data as a replacement for real-world data without cross-validation or any quality control measures.
C) Select the pre-trained model, generate synthetic data, inspect the generated data for quality, and fine-tune the model by adjusting hyperparameters and training settings.
D) Select the pre-trained model, generate synthetic data, and fine-tune the model using default parameters without further customization.

4. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A) Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
B) Top-k sampling selects the token with the highest probability, ignoring all other token options.
C) Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
D) Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.

5. A financial services company is building a generative AI model to assist with customer support. The company is concerned about potential legal liabilities if the model generates customer information, such as bank account numbers or personal identification data, as part of its responses.
Which of the following techniques would best mitigate the risk of generating Personally Identifiable Information (PII) during inference?

A) Set a strict token limit to prevent the model from generating long sequences, assuming PII tends to appear in longer outputs.
B) Train the model on sensitive customer data but ensure that the temperature is set low to avoid generating diverse outputs.
C) Use greedy decoding to ensure the model generates only the most probable tokens, which are less likely to include PII.
D) Implement a real-time PII filter that detects and removes sensitive data before the output is presented to the user.

Solutions: