- A
Cloud Vision API for object detection and OCR; Cloud Document AI for structured document extraction.
Vision API provides image analysis (object detection, OCR, label detection) using pre-trained models. Document AI specializes in extracting structured information from forms and documents. Both require zero ML training.
- B
BigQuery ML for both use cases — it trains vision models on image data stored in BigQuery.
Why wrong: BigQuery ML builds regression, classification, and forecasting models from tabular data. It cannot process images or perform computer vision tasks.
- C
Vertex AI AutoML Vision — train a custom model on your own images.
Why wrong: AutoML Vision trains custom models on your labeled image data. The question asks for pre-trained APIs that work without training — Vision API serves this need.
- D
Cloud Natural Language API for text extraction from images.
Why wrong: The Natural Language API analyzes text content (sentiment, entities, syntax) but does not process images or perform OCR. Vision API provides OCR.
Cloud Vision API and Document AI for Vision
This GCDL practice question tests your understanding of google cloud products, services, and solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to use pre-trained Google AI models to add vision capabilities to their application — specifically to detect objects in images and extract text from scanned documents — without training their own models. Which Google Cloud APIs provide these capabilities?
Quick Answer
The answer is Cloud Vision API for object detection and OCR, and Cloud Document AI for structured document extraction. This is correct because Cloud Vision API offers pre-trained models that can detect objects in images and perform Optical Character Recognition to extract text, while Cloud Document AI goes further by parsing scanned documents to extract structured data like fields and tables—both without requiring any custom model training. On the Google Cloud Digital Leader exam, this question tests your understanding of how pre-trained AI services differ in scope: Cloud Vision handles general image analysis, whereas Document AI is specialized for document-centric workflows. A common trap is assuming Cloud Vision alone can handle all document extraction, but it lacks the structured output capabilities of Document AI. Memory tip: think “Vision for visuals, Document for data”—if you need to pull out specific fields from a form, choose Document AI.
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
Cloud Vision API for object detection and OCR; Cloud Document AI for structured document extraction.
Option A is correct because Cloud Vision API provides pre-trained models for object detection and OCR (Optical Character Recognition) to extract text from images, while Cloud Document AI specializes in extracting structured data (e.g., fields, tables) from scanned documents. Both services require no custom training, aligning with the company's requirement to use pre-trained Google AI models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud Vision API for object detection and OCR; Cloud Document AI for structured document extraction.
Why this is correct
Vision API provides image analysis (object detection, OCR, label detection) using pre-trained models. Document AI specializes in extracting structured information from forms and documents. Both require zero ML training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery ML for both use cases — it trains vision models on image data stored in BigQuery.
Why it's wrong here
BigQuery ML builds regression, classification, and forecasting models from tabular data. It cannot process images or perform computer vision tasks.
- ✗
Vertex AI AutoML Vision — train a custom model on your own images.
Why it's wrong here
AutoML Vision trains custom models on your labeled image data. The question asks for pre-trained APIs that work without training — Vision API serves this need.
- ✗
Cloud Natural Language API for text extraction from images.
Why it's wrong here
The Natural Language API analyzes text content (sentiment, entities, syntax) but does not process images or perform OCR. Vision API provides OCR.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Cloud Natural Language API with OCR capabilities, or assume BigQuery ML can handle image data, when in fact Google Cloud separates vision and text analysis into distinct APIs with specific pre-trained models.
Detailed technical explanation
How to think about this question
Cloud Vision API uses pre-trained deep neural networks (e.g., ResNet-based architectures) to detect objects via bounding boxes and labels, and employs OCR engines (like Tesseract-based or proprietary) to extract text from images. Cloud Document AI leverages specialized models (e.g., Form Parser) that handle layout analysis and key-value pair extraction from structured documents like invoices or forms, using techniques like spatial attention and transformer-based OCR. In a real-world scenario, a company scanning receipts would use Cloud Vision API for raw text extraction and Cloud Document AI to parse the receipt into fields like total, date, and vendor.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this GCDL question test?
Google Cloud products, services, and solutions — This question tests Google Cloud products, services, and solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Cloud Vision API for object detection and OCR; Cloud Document AI for structured document extraction. — Option A is correct because Cloud Vision API provides pre-trained models for object detection and OCR (Optical Character Recognition) to extract text from images, while Cloud Document AI specializes in extracting structured data (e.g., fields, tables) from scanned documents. Both services require no custom training, aligning with the company's requirement to use pre-trained Google AI models.
What should I do if I get this GCDL question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This GCDL 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 GCDL exam.
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