SOL-C01 Question #207: Real Exam Question with Answer & Explanation

Question

You're using Snowflake Cortex's CLASSIFY TEXT function to analyze customer survey responses. A particular survey question asks respondents to list their favorite features of a product. You want to use CLASSIFY TEXT to categorize the responses, but many responses contain multiple distinct features. How can you best leverage CLASSIFY TEXT to classify each feature mentioned in a single response individually?

Options

• AUse a regular expression UDF to split each survey response into individual sentences (assuming
• BConcatenate all survey responses into a single large string and apply CLASSIFY_TEXT to this
• CApply CLASSIFY _ TEXT directly to each full survey response. The function is designed to handle
• DThere is no way to classify multiple features within a single response using CLASSIFY TEXT. The
• EUse a Python UDF that leverages an NLP library (e.g., spaCy) to extract individual features from

Explanation

Option E is the most effective approach. Using a Python UDF with NLP libraries like spacy allows for accurate extraction of individual features, enabling CLASSIFY TEXT to categorize each one separately. Option A might work in some cases but is unreliable as not all features are discussed in separate sentences. Option B is incorrect because CLASSIFY _ TEXT is meant to classify the sentiment behind the text and not extract features from it. Option C isn't best because the function isn't made to determine multiple classifications for multiple topics. Option D isn't correct because if the features can be extracted, the TEXT_CLASSIFY function can determine

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