- A
Extracting the most frequently used words from a speech transcript
Why wrong: Word frequency analysis is text analytics — keyword recognition continuously listens for a specific activation word.
- B
Continuously listening for a specific wake word to activate full speech processing without cloud round-trips
Keyword recognition enables always-on local detection — triggering full cloud speech processing only when the wake word is heard.
- C
Highlighting important keywords in a speech transcript for meeting notes
Why wrong: Transcript keyword highlighting is a meeting intelligence feature — keyword recognition is a speech activation mechanism.
- D
Detecting when a customer uses specific product keywords during a support call
Why wrong: Call analytics is a contact centre application — keyword recognition is the specific capability of local always-on wake word detection.
Quick Answer
The correct answer is that Azure AI Speech’s keyword recognition continuously listens for a specific wake word to activate full speech processing without cloud round-trips. This is correct because the service runs a lightweight, always-on keyword spotter locally on the device, which triggers the heavier speech-to-text pipeline only when the predefined phrase—such as “Hey Cortana”—is heard. By avoiding constant cloud connectivity, it conserves bandwidth, reduces latency, and preserves device resources. On the AI-900 exam, this concept tests your understanding of edge-based vs. cloud-based processing, often appearing in scenario questions about hands-free or always-listening applications like smart assistants or voice-activated kiosks. A common trap is confusing keyword recognition with continuous speech recognition—remember that keyword recognition is the trigger, not the full transcription. Memory tip: think of it as a “doorman” that only opens the door for the VIP wake word, keeping the party inside quiet until then.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'Azure AI Speech's keyword recognition' and what are its use cases?
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
Continuously listening for a specific wake word to activate full speech processing without cloud round-trips
Azure AI Speech's keyword recognition is designed to continuously listen for a specific wake word (e.g., 'Hey Cortana') and activate full speech processing only when that keyword is detected. This allows the system to remain idle until triggered, reducing unnecessary cloud round-trips and conserving bandwidth and processing resources.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Extracting the most frequently used words from a speech transcript
Why it's wrong here
Word frequency analysis is text analytics — keyword recognition continuously listens for a specific activation word.
- ✓
Continuously listening for a specific wake word to activate full speech processing without cloud round-trips
Why this is correct
Keyword recognition enables always-on local detection — triggering full cloud speech processing only when the wake word is heard.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Highlighting important keywords in a speech transcript for meeting notes
Why it's wrong here
Transcript keyword highlighting is a meeting intelligence feature — keyword recognition is a speech activation mechanism.
- ✗
Detecting when a customer uses specific product keywords during a support call
Why it's wrong here
Call analytics is a contact centre application — keyword recognition is the specific capability of local always-on wake word detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing keyword recognition (a local, always-on wake word detector) with key phrase extraction or custom keyword spotting in the cloud, leading candidates to pick options that describe post-processing or cloud-dependent analysis.
Trap categories for this question
Keyword trap
Word frequency analysis is text analytics — keyword recognition continuously listens for a specific activation word.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Speech's keyword recognition uses a lightweight, on-device model (often based on a deep neural network) that runs locally to detect the wake word, triggering the full speech-to-text pipeline only after activation. This design is critical for always-on scenarios like smart speakers or voice assistants, where sending every audio snippet to the cloud would be impractical due to latency and cost. The system can be customized using the 'Custom Keyword' feature in Speech Studio, allowing developers to define unique wake words with a minimum of two syllables for reliable detection.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of Natural Language Processing workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of Natural Language Processing workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Continuously listening for a specific wake word to activate full speech processing without cloud round-trips — Azure AI Speech's keyword recognition is designed to continuously listen for a specific wake word (e.g., 'Hey Cortana') and activate full speech processing only when that keyword is detected. This allows the system to remain idle until triggered, reducing unnecessary cloud round-trips and conserving bandwidth and processing resources.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.