Quick Answer
The answer is Azure AI Document Intelligence and Azure AI Language. This is correct because a document processing pipeline for OCR and NER requires two distinct capabilities: first, extracting raw text from scanned PDFs using optical character recognition, which Azure AI Document Intelligence handles with its read and layout models, and second, identifying named entities like people, organizations, and locations from that extracted text, which Azure AI Language accomplishes through its pre-built NER feature. On the AI-102 exam, this scenario tests your ability to distinguish between Azure AI services that process visual documents versus those that analyze text, often appearing as a paired-service question where a common trap is selecting Azure AI Vision for OCR instead of Document Intelligence. Remember the pipeline flow: Document Intelligence reads the image, Language extracts the entities—think of it as "Doc reads, Language speaks."
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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.
Your organization wants to implement a document processing pipeline that extracts text from scanned PDFs and identifies named entities. Which two Azure AI services should you use?
Azure AI Language
Provides named entity recognition to identify entities in text.
Azure AI Custom Vision
Why wrong: Classifies images but does not extract text or entities.
Azure AI Document Intelligence
Extracts text from scanned documents using OCR.
Azure AI Translator
Why wrong: Translates text but does not extract or recognize entities.
Azure AI Speech
Why wrong: Converts speech to text and vice versa, not for document OCR.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure AI Language
Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure AI Language
Why this is correct
Provides named entity recognition to identify entities in text.
Related concept
Read the scenario before looking for a memorised answer.
Azure AI Custom Vision
Why it's wrong here
Classifies images but does not extract text or entities.
Azure AI Document Intelligence
Why this is correct
Extracts text from scanned documents using OCR.
Related concept
Read the scenario before looking for a memorised answer.
Azure AI Translator
Why it's wrong here
Translates text but does not extract or recognize entities.
Azure AI Speech
Why it's wrong here
Converts speech to text and vice versa, not for document OCR.
Common exam traps
The trap here is that candidates often confuse Azure AI Document Intelligence with Azure AI Custom Vision, thinking Custom Vision can perform OCR, but Document Intelligence is the dedicated service for document text extraction and layout analysis.
Detailed technical explanation
Azure AI Document Intelligence uses the Read OCR engine (based on the latest GPT-4o vision model) to extract text from scanned PDFs, including handwritten and printed content. The extracted text can then be passed to Azure AI Language's NER API, which uses a transformer-based model to identify entities with confidence scores. In a real-world pipeline, you would call Document Intelligence's 'analyze' operation on the PDF, then feed the resulting 'content' field into Language's 'entities recognition' endpoint.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
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FAQ
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Azure AI Language — Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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