PMLE Architecting Low-Code ML Solutions Practice Question
A company wants to classify customer support emails into categories like 'billing', 'technical', or 'account'. They have labeled email text data. Which AutoML solution should they use?
⚠ Common exam trap
PMLE often tests the distinction between AutoML services based on data modality; candidates may confuse AutoML Natural Language with AutoML Tables when the data is text but stored in a tabular format.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AutoML Natural Language
AutoML Natural Language is designed for text classification tasks, including sentiment analysis, entity extraction, and content categorization. Since the company has labeled email text data and wants to classify emails into categories like 'billing', 'technical', or 'account', AutoML Natural Language is the correct choice. It handles text data natively and provides a simple interface to train custom models without requiring deep ML expertise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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AutoML Tables
Why it's wrong here
AutoML Tables builds models on structured, tabular data, so raw email text cannot be ingested as features. It is tempting because it handles classification tasks, and would be correct for predicting a label from columns such as numeric or categorical customer attributes.
- ✓
AutoML Natural Language
Why this is correct
AutoML Natural Language performs text classification on labelled data, training a model that assigns support emails to categories such as billing, technical or account, matching the labelled email text and multi-class requirement in the stem.
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AutoML Video
Why it's wrong here
AutoML Video classifies and tracks content within video files, so email text cannot be used as training input. It is tempting because it performs classification, and would be correct for labelling actions, objects, or scenes in video footage.
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AutoML Vision
Why it's wrong here
AutoML Vision processes image pixels, so it cannot ingest the labelled email text this scenario supplies. It is tempting because it genuinely classifies visual content — for example sorting scanned invoices or product photos into categories — but that requires image data, not the text corpus described here.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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