Question 38 of 300
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GCDL Practice Question: Use pre-trained Google AI models to add vision…

This GCDL practice question tests your understanding of use pre-trained google ai models to add vision…. 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?

Question 1easymultiple choice
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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?

Answer choices

Why each option matters

Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.

A

Distractor review

BigQuery ML for both use cases — it trains vision models on image data stored in BigQuery.

BigQuery ML builds regression, classification, and forecasting models from tabular data. It cannot process images or perform computer vision tasks.

B

Distractor review

Vertex AI AutoML Vision — train a custom model on your own images.

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.

C

Best answer

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.

D

Distractor review

Cloud Natural Language API for text extraction from images.

The Natural Language API analyzes text content (sentiment, entities, syntax) but does not process images or perform OCR. Vision API provides OCR.

Common exam trap

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Technical deep dive

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

  • Static NAT maps one inside address to one outside address.
  • PAT allows many inside hosts to share one public address using ports.
  • Inside local and inside global describe the private and translated addresses.
  • NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

  • Identify inside and outside interfaces first.
  • Check whether the scenario needs static NAT, dynamic NAT or PAT.
  • Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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FAQ

Questions learners often ask

What does this GCDL question test?

Static NAT maps one inside address to one outside address.

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. — Google Cloud provides pre-trained AI APIs that developers can call without any ML expertise or model training: Cloud Vision API detects objects, labels, faces, and reads text (OCR) from images. Cloud Document AI extracts structured data from documents. These APIs encapsulate Google's ML research and are accessible via simple REST API calls, enabling any developer to add AI capabilities to their applications.

What should I do if I get this GCDL question wrong?

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related GCDL NAT questions on configuration and troubleshooting.

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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.