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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q230-Q235):

NEW QUESTION # 230
In a scenario where a developer is creating reusable prompt templates for a Watsonx Generative AI project, what is the most effective method to track the usage and performance of these templates over time?

A. Using static prompt templates without any tracking, as tracking adds unnecessary overhead
B. Embedding unique identifiers in the prompt templates and using Watsonx's logging mechanisms
C. Leveraging Watsonx's native analytics and monitoring tools with built-in prompt tracking features
D. Relying on manual tracking of templates in a spreadsheet for each generation

Answer: B

NEW QUESTION # 231
You are tasked with integrating third-party embedding models into a Retrieval-Augmented Generation (RAG) system for document retrieval. Several models offer pre-trained embeddings that can be leveraged for a variety of downstream tasks.
Which of the following third-party models is designed for generating embeddings that capture semantic meaning and context, making it ideal for a RAG-based GenAI system?

A. BERT
B. Naive Bayes Classifier
C. GPT-3 Embeddings
D. WordNet

Answer: A

NEW QUESTION # 232
A client is planning to deploy a Watsonx Generative AI model and has raised concerns about ethical usage, bias, and accountability in decision-making.
Which of the following is the most critical step to ensure AI governance during the deployment phase of the model?

A. Implementing a feedback loop for continuous model improvement
B. Testing the model's accuracy on a large set of random data
C. Training the model on additional data to improve accuracy
D. Monitoring and auditing AI decisions for bias and fairness

Answer: D

NEW QUESTION # 233
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. Use a post-generation filter to remove any text that appears to be personal information
B. Leverage regular expressions to filter out personal information in the prompt
C. Embed privacy-sensitive heuristics in the model's prompt to guide its behavior
D. Train the model exclusively on datasets that contain no personal information

Answer: C

NEW QUESTION # 234
You are using IBM Watsonx to generate answers for a customer service chatbot. However, the responses generated sometimes include fabricated details that are inaccurate (hallucinations).
Which of the following strategies will best mitigate the risk of hallucination when designing your prompt?

A. Ask open-ended questions without providing reference information: "What are the latest features of our product?"
B. Supply explicit product details and constraints in the prompt: "List the latest features of the XYZ smartphone released in 2024, including its AI-powered camera and battery-saving mode."
C. Rely on the AI model to fact-check and correct hallucinations in its responses automatically: "What can you tell me about our product?"
D. Use subjective language in your prompts: ":Can you describe why our product is the best on the market?"

Answer: B

NEW QUESTION # 235
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