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.
A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Why wrong: Anomaly Detector is for identifying unusual patterns in time-series data, not for language translation or sentiment analysis.
B
Azure AI Translator and Azure AI Language (Text Analytics)
Translator detects language and translates; Azure AI Language provides language detection (if needed separately) and sentiment analysis on the translated text.
C
Azure AI Speech and Azure AI Language (Text Analytics)
Why wrong: Speech services are for audio-to-text or text-to-speech, but the input is already text; translation would still be missing.
D
Azure AI Vision and Azure AI Language (Text Analytics)
Why wrong: Vision services analyze images, not text; translation is not provided by Vision.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Translator and Azure AI Language (Text Analytics)
Option B is correct because the pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.
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.
✗
Azure AI Translator and Azure Anomaly Detector
Why it's wrong here
Anomaly Detector is for identifying unusual patterns in time-series data, not for language translation or sentiment analysis.
✓
Azure AI Translator and Azure AI Language (Text Analytics)
Why this is correct
Translator detects language and translates; Azure AI Language provides language detection (if needed separately) and sentiment analysis on the translated text.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure AI Speech and Azure AI Language (Text Analytics)
Why it's wrong here
Speech services are for audio-to-text or text-to-speech, but the input is already text; translation would still be missing.
✗
Azure AI Vision and Azure AI Language (Text Analytics)
Why it's wrong here
Vision services analyze images, not text; translation is not provided by Vision.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure AI Language (Text Analytics) with Azure AI Speech or Azure AI Vision, mistakenly thinking speech or vision services can handle text-based language detection and sentiment analysis, when in fact only Text Analytics provides those NLP capabilities.
Detailed technical explanation
How to think about this question
Azure AI Translator uses a neural machine translation (NMT) model that can detect the source language automatically via the 'detect' method in the REST API, returning an ISO 639-1 language code. Azure AI Language's sentiment analysis uses a pre-trained model that assigns a sentiment score (0 to 1) for positive, neutral, and negative classes, and can be called via the 'AnalyzeSentiment' operation. In a real-world pipeline, you would chain these calls: first call Translator to detect and translate, then pass the translated text to Text Analytics for sentiment.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Azure AI Translator and Azure AI Language (Text Analytics) — Option B is correct because the pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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