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Generative AI Leader Practice Question: A developer is building a real-time speech…
A developer is building a real-time speech transcription application for customer support calls. The audio is streamed, and the transcription must be returned with low latency. Which Google Cloud AI service should they use?
⚠ Common exam trap
Generative AI Leader often tests service-selection confusion — the trap is picking Natural Language API thinking it handles audio, or Text-to-Speech by reversing the direction, when only Speech-to-Text with streaming mode provides real-time transcription.
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
✓
Cloud Speech-to-Text with streaming recognition
Cloud Speech-to-Text supports streaming recognition via gRPC or the streaming REST endpoint, returning partial transcripts as audio arrives, which is exactly what low-latency real-time transcription requires. It handles telephony audio with models tuned for phone calls and supports interim results for responsiveness.
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 it's wrong here
Natural Language API performs text analytics such as entity, sentiment and syntax extraction on already-written text; it accepts no audio stream. It is tempting because it is a Google Cloud AI service handling language, but speech-to-text conversion requires the Speech-to-Text API's streaming recognition.
- ✓
Cloud Speech-to-Text with streaming recognition
Why this is correct
Streaming recognition accepts an ongoing audio stream and returns interim and final transcripts as speech arrives, rather than requiring a complete file upload. That incremental processing is what meets the low-latency requirement for live customer support calls.
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Cloud Text-to-Speech
Why it's wrong here
Cloud Text-to-Speech synthesises written text into spoken audio, the reverse direction of this scenario's requirement. It is tempting because it handles audio and supports streaming, but it consumes text and emits speech; transcribing streamed call audio with low latency needs Speech-to-Text's streaming recognition instead.
- ✗
Vertex AI with a custom model
Why it's wrong here
Vertex AI hosts custom model training and serving, so building a bespoke model adds training and deployment latency rather than providing managed streaming recognition. It is tempting because custom models can be tuned for domain vocabulary, but the stem's low-latency streaming requirement is met by the pre-trained Speech-to-Text API.
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Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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 Generative AI Leader 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 Generative AI Leader exam.