The correct approach is to use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction. This is the right choice because the pre-built invoice model already handles document layout and field detection, and you can extend it with a custom regex pattern—such as `[A-Z]{3}-\d{5}`—to precisely extract product codes like ‘PRD-12345’ without building a custom model from scratch. On the AI-102 exam, this question tests your understanding of how Document Intelligence’s pre-built models support custom field extraction via regex, a key skill for structured data extraction from forms and invoices. A common trap is to overcomplicate the solution by reaching for Azure AI Language’s custom NER, which lacks layout awareness, or Azure OpenAI, which is unreliable for rigid patterns. Remember the memory tip: “Pre-built plus regex equals precise extraction” — when you have a fixed pattern and a known document type, extend the pre-built model rather than building a custom one.
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. 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.
You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "always"
Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction
Option C is correct because Azure AI Document Intelligence's pre-built invoice model can be extended with custom fields using regex to extract product codes. Option A is wrong because Azure AI Language's custom NER does not handle document layouts. Option B is wrong because Azure AI Search's custom skills require additional development. Option D is wrong because Azure OpenAI GPT-4 is overkill and less reliable for structured extraction.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction
Why this is correct
Document Intelligence supports regex-based field extraction in custom models.
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Build a custom skill in Azure AI Search using a Python regex
Why it's wrong here
Custom skills are for enrichment, not direct extraction from documents.
✗
Train a custom NER model in Azure AI Language
Why it's wrong here
Custom NER is for text-based documents, not scanned invoices.
✗
Use Azure OpenAI GPT-4 with document vision to extract the codes
Why it's wrong here
GPT-4 with vision can extract but is less reliable for pattern-based extraction and more expensive.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction — Option C is correct because Azure AI Document Intelligence's pre-built invoice model can be extended with custom fields using regex to extract product codes. Option A is wrong because Azure AI Language's custom NER does not handle document layouts. Option B is wrong because Azure AI Search's custom skills require additional development. Option D is wrong because Azure OpenAI GPT-4 is overkill and less reliable for structured extraction.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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