PMLE Architecting Low-Code ML Solutions Practice Question
A hospital wants to build a system that automatically transcribes doctors' dictated notes into text and then identifies key medical terms such as diagnoses and medications. They have no ML expertise and want to use Google Cloud's pre-trained APIs. Which combination of services should they use?
⚠ Common exam trap
Candidates often confuse Text-to-Speech with Speech-to-Text, or assuming that general Natural Language API can handle medical terminology as well as the specialized Healthcare Natural Language API.
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
✓
Speech-to-Text API for transcription and Healthcare Natural Language API for medical term extraction.
The hospital needs two capabilities: converting audio to text and extracting medical terms. Speech-to-Text API handles audio transcription with high accuracy, and Healthcare Natural Language API is purpose-built for medical text analysis, identifying entities like medications and diagnoses. Both are pre-trained, requiring no ML expertise, and integrate easily. Other options use incorrect services for transcription or require custom model training, which is unnecessary given the availability of specialized pre-trained APIs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Speech-to-Text API for transcription and AutoML Natural Language for custom entity extraction.
Why it's wrong here
While Speech-to-Text is correct, AutoML Natural Language requires training a custom model with labeled data, which demands ML expertise and effort. The hospital has no ML expertise and likely lacks labeled medical data. The Healthcare Natural Language API is pre-trained for medical entities and would be more appropriate and faster to implement.
- ✓
Speech-to-Text API for transcription and Healthcare Natural Language API for medical term extraction.
Why this is correct
Speech-to-Text API converts audio to text accurately, and the Healthcare Natural Language API is specifically designed to extract medical entities like diagnoses and medications from text. This combination uses fully managed pre-trained models, requiring no ML expertise, and directly addresses both transcription and medical term identification in a single pipeline.
- ✗
Text-to-Speech API for transcription and Natural Language API for entity extraction.
Why it's wrong here
Text-to-Speech converts text to audio, which is the opposite of transcription. The Natural Language API can extract general entities but is not specialized for medical terminology, so it may miss domain-specific terms like medication names or diagnoses. This combination does not meet the requirements and uses an incorrect service for transcription.
- ✗
Dialogflow for transcription and Healthcare Natural Language API for medical term extraction.
Why it's wrong here
Dialogflow is designed for building conversational interfaces, not for transcribing audio recordings. It cannot transcribe doctors' notes. While Healthcare Natural Language API is correct for term extraction, the transcription component is wrong. This option would fail to produce accurate transcriptions from audio files.
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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.