PMLE Architecting Low-Code ML Solutions Practice Question
A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?
⚠ Common exam trap
It's easy for candidates to confuse AutoML Vision (custom model training with minimal effort) with Vision API (pre-trained, no custom training), often picking D because both involve 'Vision' and seem low-code, but Vision API cannot be retrained on custom data.
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
✓
AutoML Vision
AutoML Vision is the correct choice because it allows users with no prior ML experience to train a custom image classification model using a simple graphical interface, requiring only labeled images as input. It automates model architecture search, hyperparameter tuning, and deployment, minimizing manual effort while delivering a production-ready model.
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 TPU
Why it's wrong here
Cloud TPU provides hardware accelerators for training and serving large TensorFlow or JAX models; it supplies compute, not a managed training workflow or AutoML capability. It is tempting because it removes infrastructure effort for heavy training, and would be correct if the team already had ML expertise and needed to accelerate an existing model's training.
- ✓
AutoML Vision
Why this is correct
AutoML Vision trains custom image classifiers through a point-and-click interface, using transfer learning on Google's pretrained models. This satisfies the stem's constraints: labelled plant images, minimal effort, and no ML expertise. Vertex AI Vision now supersedes it, but AutoML Vision remains the purpose-built answer for codeless custom classification.
- ✗
Vertex AI Workbench with a custom TensorFlow model
Why it's wrong here
Vertex AI Workbench is a managed notebook environment where the team writes and runs its own TensorFlow training code; it provides no automated model selection, so prior ML experience is still required. It is tempting because it is a managed Google Cloud ML tool, and would be correct if the team wanted full control over a bespoke architecture.
- ✗
Vision API
Why it's wrong here
Vision API serves pre-trained models for generic label detection, OCR and facial attributes; it cannot be trained on the company's labelled plant images. It is tempting because it is a no-code, minimal-effort image service, and would be correct if the requirement were classifying images into the fixed categories its pre-trained models already cover.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.