Question 800 of 1,020

Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct combination is Language Detection, Custom Text Classification, and Entity Recognition because the scenario demands three distinct, sequential NLP tasks that Azure AI Language handles as separate but combinable features. Language Detection first identifies the email’s language, which is a prerequisite for accurate downstream processing, then Custom Text Classification routes the email to the correct department (Billing, Technical Support, or Returns) by learning from labeled examples, and finally Entity Recognition extracts the order number and product name as predefined entities. On the AI-900 exam, this question tests your ability to map real-world requirements to specific Azure AI Language capabilities, often appearing in scenario-based multiple-choice questions where a common trap is to confuse Custom Text Classification with built-in Sentiment Analysis or Key Phrase Extraction. Remember the pipeline order: detect the language first, classify the intent second, and extract the details third. A useful mnemonic is “Detect, Classify, Extract” — or DCE — to recall the three steps in sequence.

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 e-commerce company receives customer support emails in over 30 languages. They want to automatically route each email to the correct department (Billing, Technical Support, or Returns) and also extract the order number and the product name mentioned in the email. The solution must be able to identify the language of each email before further processing. Which combination of Azure AI Language features should they use?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Language Detection, Custom Text Classification, and Entity Recognition
Correct. Language Detection identifies the email's language. Custom Text Classification enables routing to departments. Entity Recognition extracts the order number and product name as entities.
B
Language Detection, Key Phrase Extraction, and Sentiment Analysis
Why wrong: Incorrect. Key Phrase Extraction extracts important phrases but not specific entities like order numbers. Sentiment Analysis provides sentiment polarity, not department routing.
C
Entity Recognition, Sentiment Analysis, and Key Phrase Extraction
Why wrong: Incorrect. This combination lacks Language Detection (required) and Custom Text Classification for routing. Sentiment Analysis does not classify into departments.
D
Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis
Why wrong: Incorrect. While Custom Text Classification is valid for routing, Key Phrase Extraction cannot be relied upon to extract specific structured entities like order numbers. Entity Recognition is needed for that.

Full breakdown with real-world context →

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

✓

Language Detection, Custom Text Classification, and Entity Recognition

Option A is correct because the scenario requires three distinct capabilities: Language Detection to identify the email's language (a prerequisite for further processing), Custom Text Classification to route emails to the correct department (Billing, Technical Support, or Returns), and Entity Recognition to extract the order number and product name. Azure AI Language provides these as separate, combinable features that directly map to the stated 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.

✓
Language Detection, Custom Text Classification, and Entity Recognition
Why this is correct
Correct. Language Detection identifies the email's language. Custom Text Classification enables routing to departments. Entity Recognition extracts the order number and product name as entities.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Detection, Key Phrase Extraction, and Sentiment Analysis
Why it's wrong here
Incorrect. Key Phrase Extraction extracts important phrases but not specific entities like order numbers. Sentiment Analysis provides sentiment polarity, not department routing.
✗
Entity Recognition, Sentiment Analysis, and Key Phrase Extraction
Why it's wrong here
Incorrect. This combination lacks Language Detection (required) and Custom Text Classification for routing. Sentiment Analysis does not classify into departments.
✗
Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis
Why it's wrong here
Incorrect. While Custom Text Classification is valid for routing, Key Phrase Extraction cannot be relied upon to extract specific structured entities like order numbers. Entity Recognition is needed for that.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse Key Phrase Extraction with Entity Recognition, or assume Sentiment Analysis is needed for routing, when in fact the scenario's explicit requirements (language identification, department routing, and specific entity extraction) map directly to Language Detection, Custom Text Classification, and Entity Recognition.

Trap categories for this question

Keyword trap
Incorrect. Key Phrase Extraction extracts important phrases but not specific entities like order numbers. Sentiment Analysis provides sentiment polarity, not department routing.

Detailed technical explanation

How to think about this question

Azure AI Language's Language Detection uses a deep neural network model trained on over 100 languages, returning a language name and ISO 639-1 code. Custom Text Classification requires a pre-labeled dataset to train a model that assigns emails to predefined categories (e.g., Billing), while Entity Recognition (prebuilt or custom) can extract order numbers and product names using regex-like patterns or learned models. In practice, the pipeline would first call Language Detection, then route via Custom Text Classification, and finally extract entities from the email body.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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.

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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: Language Detection, Custom Text Classification, and Entity Recognition — Option A is correct because the scenario requires three distinct capabilities: Language Detection to identify the email's language (a prerequisite for further processing), Custom Text Classification to route emails to the correct department (Billing, Technical Support, or Returns), and Entity Recognition to extract the order number and product name. Azure AI Language provides these as separate, combinable features that directly map to the stated 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.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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