PMLE Architecting Low-Code ML Solutions Practice Question
A company needs to extract key fields from scanned invoices, such as invoice number and total amount, with high accuracy. They want a managed service and plan to use human review for low-confidence results. Which combination of services should they use?
⚠ Common exam trap
PMLE often tests the confusion between generic Vision/NLP APIs and purpose-built Document AI — candidates assume any OCR service can extract invoice fields, missing that schema-aware extraction plus a native human-review loop is the differentiator.
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 and Human-in-the-Loop
Document AI is Google Cloud's managed document understanding service with specialized parsers (Invoice, Expense, Form) that extract structured fields like invoice number and total amount with high accuracy. Human-in-the-Loop (HITL) integrates directly with Document AI to route low-confidence predictions to human reviewers, whose corrections feed back to improve the processor. Together they satisfy the managed-service and human-review requirements in one pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vision API and Natural Language API
Why it's wrong here
Vision API performs OCR and Natural Language API analyses text entities, but neither returns invoice-specific key-value pairs with confidence for review. It tempts because both are managed and handle documents, and Vision OCR alone would suit plain text extraction without structured fields.
- ✓
Document AI and Human-in-the-Loop
Why this is correct
Document AI extracts invoice fields via prebuilt or custom models, returning per-field confidence scores. Human-in-the-Loop routes only low-confidence extractions to reviewers, satisfying the stem's accuracy requirement while keeping the service fully managed. This pairing directly matches the stated need for human review of uncertain results.
- ✗
Translation API and AutoML Vision
Why it's wrong here
Translation API converts languages and AutoML Vision classifies images, neither extracting structured invoice fields with confidence scores. It tempts because both are managed AI services, and AutoML Vision would suit custom image classification where labels, not field values, are the goal.
- ✗
BigQuery ML and Vertex AI Prediction
Why it's wrong here
BigQuery ML trains models on tabular data and Vertex AI Prediction serves them; neither parses document layout to extract fields. It tempts because both are managed ML offerings, and Vertex AI would be right for deploying a custom-trained extraction model.
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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.