- A
Pre-built models are free; custom models have additional training costs
Why wrong: Pricing exists for both — the primary distinction is capability fit: general vs. domain-specific.
- B
Pre-built models need no training for general tasks; custom models train on your data for specialised needs
Pre-built = instant, general purpose. Custom = trained on your labels for domain-specific entity types and categories.
- C
Pre-built models only work in English; custom models support all languages
Why wrong: Pre-built models support 25+ languages — the distinction is domain specificity, not language coverage.
- D
Custom models are always more accurate than pre-built regardless of the use case
Why wrong: Custom models excel for specific domains — for general tasks with standard language, pre-built models can match or exceed custom performance.
Quick Answer
The correct choice is pre-built models for general tasks and custom models for specialized needs, because Azure AI Language offers pre-built models that are immediately ready for common NLP tasks like sentiment analysis, key phrase extraction, and language detection without requiring any training data, while custom models demand that you upload and label your own dataset to train a model for niche requirements such as custom entity recognition or custom text classification that pre-built models cannot handle. On the AI-900 exam, this distinction tests your understanding of when to leverage out-of-the-box capabilities versus when to invest in tailored solutions—a common trap is assuming pre-built models can be retrained, but they are static and only custom models allow domain-specific tuning. Remember the memory tip: “Pre-built for the common, custom for the uncommon.”
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 Language's pre-built models' vs 'custom models' and when do you choose each?
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
Pre-built models need no training for general tasks; custom models train on your data for specialised needs
Option B is correct because Azure AI Language provides pre-built models that are ready to use for common NLP tasks like sentiment analysis, key phrase extraction, and language detection without any training. Custom models, on the other hand, require you to upload your own labeled data and train a model to handle specialized needs, such as custom entity recognition or custom text classification, which pre-built models cannot address.
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.
- ✗
Pre-built models are free; custom models have additional training costs
Why it's wrong here
Pricing exists for both — the primary distinction is capability fit: general vs. domain-specific.
- ✓
Pre-built models need no training for general tasks; custom models train on your data for specialised needs
Why this is correct
Pre-built = instant, general purpose. Custom = trained on your labels for domain-specific entity types and categories.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Pre-built models only work in English; custom models support all languages
Why it's wrong here
Pre-built models support 25+ languages — the distinction is domain specificity, not language coverage.
- ✗
Custom models are always more accurate than pre-built regardless of the use case
Why it's wrong here
Custom models excel for specific domains — for general tasks with standard language, pre-built models can match or exceed custom performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume pre-built models are free or only support English, when in fact they are paid per use and support many languages, leading them to incorrectly eliminate Option B.
Detailed technical explanation
How to think about this question
Under the hood, pre-built models in Azure AI Language are based on large, pre-trained transformer architectures (e.g., BERT variants) that have been fine-tuned on broad corpora, making them effective for general tasks but unable to recognize domain-specific entities like 'part number XYZ-123' without custom training. Custom models use the same underlying neural network but allow you to retrain the final layers with your own labeled examples via the Custom Text Classification or Custom Named Entity Recognition features, which can significantly improve precision for niche vocabularies. A real-world scenario is a legal firm needing to extract contract clause types—pre-built models cannot identify 'force majeure' as a custom entity, but a custom model trained on legal documents can.
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.
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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: Pre-built models need no training for general tasks; custom models train on your data for specialised needs — Option B is correct because Azure AI Language provides pre-built models that are ready to use for common NLP tasks like sentiment analysis, key phrase extraction, and language detection without any training. Custom models, on the other hand, require you to upload your own labeled data and train a model to handle specialized needs, such as custom entity recognition or custom text classification, which pre-built models cannot address.
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
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Last reviewed: Jun 11, 2026
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