Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What is 'Azure AI Language's pre-built models' vs 'custom models' and when do you choose each?
⚠ Common exam trap
A common mix-up: 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.
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
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.
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
The claim that pre-built models are free is inaccurate; Azure AI Language bills per transaction for pre-built APIs, just as it bills for custom model endpoints. Custom models do add training costs because you consume compute resources during the training run, but both options involve ongoing inference charges. The substantive difference is capability fit, not whether a fee is paid.
- ✓
Pre-built models need no training for general tasks; custom models train on your data for specialised needs
Why this is correct
Pre-built models are immediately usable for common scenarios such as sentiment analysis, key phrase extraction, and language detection, with no training data required. Custom models, on the other hand, require you to label your own examples and run a training pipeline so the model learns domain-specific entities and categories. This makes custom models ideal for niche vocabularies, while pre-built models are the fastest choice for generic tasks.
- ✗
Pre-built models only work in English; custom models support all languages
Why it's wrong here
Pre-built models are not restricted to English; Azure AI Language's pre-built capabilities support over 25 languages, including Spanish, French, and German, for tasks like sentiment and key phrases. Custom models' language coverage depends entirely on the language of the labeled training data you provide, so they are not inherently multilingual. Therefore, the real distinction is domain specificity, not language support.
- ✗
Custom models are always more accurate than pre-built regardless of the use case
Why it's wrong here
Custom models are not universally more accurate; they excel when your data contains specialized jargon or unique categories that generic pre-built models were not trained on. For everyday language and standard formats, pre-built models can match or even surpass custom models because they learn from massive, broad datasets. Relative accuracy depends on how close the task is to the model's training distribution, not on the custom-versus-pre-built label itself.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Sentiment analysis
Sentiment analysis is a natural language processing technique that uses machine learning to determine the emotional tone or opinion expressed in a piece of text.
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