Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
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
Distractor review
Language Detection, Key Phrase Extraction, and Sentiment Analysis
Incorrect. Key Phrase Extraction extracts important phrases but not specific entities like order numbers. Sentiment Analysis provides sentiment polarity, not department routing.
C
Distractor review
Entity Recognition, Sentiment Analysis, and Key Phrase Extraction
Incorrect. This combination lacks Language Detection (required) and Custom Text Classification for routing. Sentiment Analysis does not classify into departments.
D
Distractor review
Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis
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 trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?
Question 2
A developer is building a customer support chatbot using Azure OpenAI. The chatbot should never reveal its system instructions or internal configuration. The developer wants to add a rule at the beginning of the conversation to prevent prompt injection attacks. Which technique should they use?
Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Language Detection, Custom Text Classification, and Entity Recognition — The scenario requires three distinct tasks: 1) Language identification (to know which language the email is in, though not strictly needed for downstream tasks like entity recognition which works across languages, but the question says 'must be able to identify the language' so Language Detection is needed). 2) Routing to a department based on content – this is a text classification task. Azure AI Language provides a Custom Text Classification feature (not pre-built sentiment or key phrases) that can be trained to classify emails into Billing, Technical Support, or Returns. Alternatively, a pre-built feature like Sentiment Analysis does not provide department categories. 3) Extracting order numbers and product names – these are specific entities, best handled by Entity Recognition (pre-built can extract generic entities like numbers and products, or custom entity extraction can be used). Pre-built Entity Recognition can identify quantities, but for custom entities like order numbers, Custom Named Entity Recognition (NER) would be more accurate, but the pre-built feature can extract 'Quantity' and 'Product' in many contexts. Given the options, the best combination is Language Detection to identify the language, Custom Text Classification for routing, and Entity Recognition to extract order numbers and product names. Key Phrase Extraction only extracts important phrases, not specific entities. Sentiment Analysis gives sentiment polarity, not routing categories. So the correct choice is Language Detection, Custom Text Classification, and Entity Recognition.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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