PMLE Architecting Low-Code ML Solutions Practice Question
A financial services company wants to extract text and structured data from scanned loan application forms. They need a fully managed, low-code solution that can handle various form layouts and requires minimal machine learning expertise. Which Google Cloud service should they use?
⚠ Common exam trap
The trap here is assuming that Vision API's OCR is sufficient, but it lacks structured extraction and form parsing capabilities.
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
✓
Document AI
Document AI provides specialized document parsing capabilities, including form understanding and entity extraction, with pre-built models and the ability to train custom extractors with minimal effort. It is fully managed and designed for low-code document processing, making it the best fit for loan application forms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Document AI
Why this is correct
Document AI is a fully managed service that uses pre-trained and customizable models to parse documents, extract text, and identify structured fields. It supports form parsing and can be used with minimal ML expertise via the console or API. It is ideal for processing loan applications with varying layouts.
- ✗
Vision API
Why it's wrong here
Vision API is designed for image analysis tasks like object detection and OCR, but it does not provide structured form parsing or entity extraction for documents. It can extract text, but not key-value pairs or tables. It would require additional custom logic to structure the data, increasing complexity.
- ✗
BigQuery ML
Why it's wrong here
BigQuery ML is for building and deploying ML models using SQL on structured data. It cannot extract data from scanned documents or images. Using it would require external OCR and preprocessing, which is not low-code and does not address the core requirement of document understanding.
- ✗
AutoML Natural Language
Why it's wrong here
AutoML Natural Language is for text classification, entity extraction, and sentiment analysis on text data. It does not process document images or forms directly. You would need to OCR the documents first, then use AutoML, which adds steps and does not provide a low-code end-to-end solution.
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About these practice questions
This PMLE question is part of Courseiva's 775-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.