PMLE Architecting Low-Code ML Solutions Practice Question
A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?
⚠ Common exam trap
The trap is overcomplicating the solution by considering custom model training (AutoML) or using image analysis frame-by-frame. The exam expects you to know that Video Intelligence API has built-in explicit content detection.
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
✓
Use Video Intelligence API with explicit content detection
Video Intelligence API provides explicit content detection specifically designed to identify violence, adult material, and other explicit content in videos. It is a managed service that scales automatically and can be integrated into video processing pipelines via its API. This is the most direct and scalable solution for moderating user-uploaded videos.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Video Intelligence API with explicit content detection
Why this is correct
The Video Intelligence API provides explicit content detection purpose-built for video, analysing frames and audio for violence and adult material. It integrates into automated pipelines and scales elastically, satisfying the stem's requirement to moderate millions of videos without building custom models.
- ✗
Use AutoML Video to train a custom explicit content detection model
Why it's wrong here
AutoML Video is incorrect because it necessitates training a custom model with a vast, labelled dataset of explicit content, which the scenario does not imply is available or desired for this generic task. The company requires an immediate, pre-trained solution for common explicit content detection. This option is tempting as AutoML simplifies custom model creation, making it suitable when detecting highly niche or proprietary content for which no off-the-shelf solution exists and a specific training dataset is readily available.
- ✗
Use Natural Language API on video transcripts
Why it's wrong here
Transcripts capture spoken words, not visual violence or adult imagery, so explicit footage with no dialogue passes moderation entirely. The Natural Language API is correct for text classification, sentiment and content-category analysis of documents or captions, not for analysing video pixels.
- ✗
Use Vision API to analyze each video frame
Why it's wrong here
Extracting and submitting every frame to the Vision API is cost-prohibitive and slow at millions of videos, and it ignores audio. Vision API suits still-image labelling and OCR; the Video Intelligence API performs shot-level explicit-content detection natively within a video pipeline.
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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.