PMLE Architecting Low-Code ML Solutions Practice Question
A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?
⚠ Common exam trap
PMLE often tests the combination of services for multi-step tasks; candidates might choose Document AI for transcription or Translation API for sentiment, but these are incorrect service mappings.
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
✓
Natural Language API
Speech-to-Text (option C) is correct because it converts audio from the customer service calls into written text, which is the required transcription step. Natural Language API (option A) is correct because it performs sentiment analysis on text, allowing the company to analyze the sentiment of the transcribed call content. Document AI (option B) is not appropriate here because it processes documents and forms rather than audio or general sentiment analysis. Translation API (option D) only translates text between languages and does not transcribe audio or analyze sentiment. Vision API (option E) analyzes images, so it cannot handle audio transcription or text sentiment analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Natural Language API
Why this is correct
The Natural Language API performs sentiment analysis on text, satisfying the requirement to analyse the transcribed call content. Speech-to-Text handles the audio transcription; Natural Language API then classifies each transcript's sentiment. Together they cover both stages of the pipeline, making this one of the two required services.
- ✗
Document AI
Why it's wrong here
Document AI extracts structured data from documents such as invoices and forms; it does not transcribe audio. Speech-to-Text handles the call recordings, and Natural Language performs sentiment analysis on the resulting text. Document AI would be the right choice for parsing scanned contracts or receipts, not spoken conversations.
- ✓
Speech-to-Text
Why this is correct
Speech-to-Text converts call audio into written transcripts using Google's speech recognition models, satisfying the transcription half of the requirement. It supports telephony audio and multiple languages, producing the text that the paired sentiment analysis service then processes. Without this conversion, the raw audio could not be analysed for sentiment.
- ✗
Translation API
Why it's wrong here
Translation API converts text between human languages; it does not extract sentiment from English transcripts. It is tempting because call analytics pipelines often include localisation, but sentiment analysis requires Natural Language API, so Translation API adds an unnecessary step that fails the stated requirement.
- ✗
Vision API
Why it's wrong here
Vision API performs label detection, OCR and facial analysis on images; it cannot process audio or derive sentiment from text. It is tempting because it is a prominent pre-trained AI service, but the scenario needs Speech-to-Text for transcription and Natural Language API for sentiment, so Vision API addresses neither task.
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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.