This GCDL practice question tests your understanding of why cloud technology is transforming business. 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 innovate quickly by leveraging machine learning without building models from scratch. Which Google Cloud service allows them to use pre-trained models via APIs?
BigQuery ML
Why wrong: BigQuery ML is for SQL-based ML on data in BigQuery.
AI Platform
Why wrong: AI Platform is for custom model training and deployment.
Cloud Vision API
Cloud Vision API offers pre-trained models via simple API calls.
AutoML
Why wrong: AutoML requires training on your own data.
Answer choices
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
Cloud Vision API is correct because it provides pre-trained machine learning models via REST APIs, allowing the company to integrate image recognition capabilities (e.g., label detection, OCR, face detection) without building or training any models. This directly meets the requirement of leveraging ML without building from scratch, as the API abstracts all model training and deployment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
BigQuery ML
Why it's wrong here
BigQuery ML is for SQL-based ML on data in BigQuery.
AI Platform
Why it's wrong here
AI Platform is for custom model training and deployment.
Cloud Vision API
Why this is correct
Cloud Vision API offers pre-trained models via simple API calls.
Related concept
Read the scenario before looking for a memorised answer.
AutoML
Why it's wrong here
AutoML requires training on your own data.
Common exam traps
Google Cloud often tests the distinction between 'pre-trained APIs' and 'custom model training services'—candidates mistakenly choose AutoML because they think 'no building from scratch' means no coding, but AutoML still requires training a custom model, not using a pre-trained one.
Detailed technical explanation
Cloud Vision API uses deep neural networks trained on massive datasets (e.g., ImageNet) to provide features like label detection, safe search, and text extraction. Under the hood, it leverages Google's TensorFlow-based models and serves predictions via gRPC or HTTP endpoints, with automatic scaling and latency optimization. In a real-world scenario, a retail company could use Cloud Vision API to automatically tag product images without any ML infrastructure, whereas AutoML would require uploading labeled images and waiting for training to complete.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Why cloud technology is transforming business — This question tests Why cloud technology is transforming business — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Vision API — Cloud Vision API is correct because it provides pre-trained machine learning models via REST APIs, allowing the company to integrate image recognition capabilities (e.g., label detection, OCR, face detection) without building or training any models. This directly meets the requirement of leveraging ML without building from scratch, as the API abstracts all model training and deployment.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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