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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

A. Providing a list of credible sources in the prompt
B. Implementing zero-shot learning techniques
C. Using more open-ended prompts
D. Increasing the model's temperature parameter

Question 2

During the deployment of a generative AI model using IBM Watsonx, you are responsible for ensuring that the model does not generate content containing hate speech, abusive language, or profanity. However, in some scenarios, the model might bypass basic filtering mechanisms and produce subtle or context-specific harmful language.
Which of the following best addresses edge cases where hate speech or abuse may slip through more basic filtering mechanisms?

A. Use IBM Watsonx's HAP (Hate, Abuse, and Profanity) filter, which is designed to handle context-specific and subtle forms of harmful language.
B. Use sentiment analysis to detect and block negative content that may indicate hate speech or abuse.
C. Implement regular expression-based filters that cover a wide range of variations in harmful language.
D. Continuously monitor and retrain the model on datasets containing edge cases of hate speech, abuse, and profanity.

Question 3

When fine-tuning a model in Tuning Studio, which of the following is a key advantage of this tool in reducing resource costs while improving model performance?

A. It optimizes the model for multilingual capabilities by adding new embeddings.
B. It automatically increases the number of layers for more complex tasks.
C. It allows incremental training, saving computational resources by reusing checkpoints.
D. It expands the model's architecture to handle larger datasets.

Question 4

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.

Question 5

You are using IBM's Tuning Studio to optimize a generative AI model. The model performance on your validation set has plateaued, and you suspect that tuning certain parameters in the Tuning Studio will improve the outcome.
Which of the following actions would be most effective in improving the model's performance?

A. Increase the number of epochs
B. Decrease the batch size
C. Use a larger validation set
D. Enable early stopping

Solutions: