IBM recommends at least how many training examples per intent for reliable classification?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Todd Lammle: 'Imagine you're building a chatbot and this exact situation comes up — at least 10; with more diverse examples being generally better is your go-to move. IBM recommends providing at least 10 diverse training examples per intent and notes that more varied examples typically improve classification accuracy. This is a classic Domain 2: Design and Extend Conversations concept you'll want locked in before exam day.'
Full explanation below image
Full Explanation
IBM recommends providing at least 10 diverse training examples per intent and notes that more varied examples typically improve classification accuracy. Three or five examples are insufficient for robust NLU performance. Ten is the minimum guideline; not the ideal maximum. The correct answer, "At least 10; with more diverse examples being generally better", directly satisfies the scenario because it aligns with watsonx Assistant's design principles and the specific capability being tested. The incorrect options ("3", "5", "10") may appear relevant but each misses a key requirement or introduces a step that is either unnecessary or belongs to a different workflow. Mastering the distinction between these approaches is essential for effective watsonx Assistant implementations and is a core focus of the Domain 2: Design and Extend Conversations section of the certification exam.