AI-102 Plan and manage an Azure AI solution Practice Question
Which TWO Azure AI services can be used together to build a solution that transcribes customer service calls and detects sentiment?
⚠ Common exam trap
It's easy for candidates to confuse Azure AI Speech's text-to-speech with speech-to-text, or mistakenly think Azure AI Translator or conversational language understanding can perform sentiment analysis, when in fact only the specific sentiment analysis feature of Azure AI Language is designed for that task.
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
✓
Azure AI Language (sentiment analysis)
Azure AI Speech (option B) is correct because its speech-to-text capability transcribes the audio of customer service calls into text, which is the required first step for this solution. Azure AI Language (option A) is correct because its sentiment analysis feature evaluates the transcribed text and returns sentiment scores/labels (positive, negative, neutral, mixed), satisfying the detection requirement. Together, B feeds transcription output into A for sentiment evaluation. Option C (Azure AI Translator) is not needed since the scenario does not require language translation. Option D (text-to-speech) is the reverse of what is needed—it synthesizes speech from text rather than transcribing calls. Option E (conversational language understanding) extracts intents and entities for conversational apps, not sentiment, so it does not fulfill the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure AI Language (sentiment analysis)
Why this is correct
Azure AI Language's sentiment analysis returns per-sentence and document-level scores, which suits transcribed call audio where emotion shifts mid-conversation. Combined with speech-to-text transcription, it satisfies the stem's requirement to detect sentiment across customer service calls, operating on the text output rather than the audio itself.
- ✓
Azure AI Speech (speech-to-text)
Why this is correct
Azure AI Speech performs speech-to-text transcription of the call audio, converting it to text. Azure AI Language then applies sentiment analysis to that transcript, so the two services together satisfy both transcription and sentiment detection.
- ✗
Azure AI Translator
Why it's wrong here
Transcription and sentiment detection require speech-to-text and language sentiment analysis; Translator only converts text between languages, so it adds no transcription or sentiment capability. It is tempting because it processes call audio output, but it would be the right choice only for translating already-transcribed text into another language.
- ✗
Azure AI Speech (text-to-speech)
Why it's wrong here
Text-to-speech converts text into synthesised audio, the reverse of transcription, so it cannot process call recordings. It suits voice assistants and accessibility narration. The stem needs speech-to-text recognition, which Azure AI Speech also provides as a separate capability.
- ✗
Azure AI Language (conversational language understanding)
Why it's wrong here
Conversational language understanding extracts intents and entities from utterances for bots and command routing; it does not classify sentiment. Sentiment analysis is a separate Azure AI Language feature. The stem's sentiment detection requires that feature, not intent recognition.
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