You need to identify files containing specific phrases using cosine similarity.
Cosine similarity is used in vector embeddings, not in generative LLM completion.
* text-embedding-ada-002 # Correct model for embeddings and cosine similarity search.
* GPT-4, GPT-3.5 Turbo, GPT-4-32k # Generative models, not designed for embedding similarity.
The answer: A. text-embedding-ada-002

When creating a voice talent profile for Custom Neural Voice in Azure Cognitive Services Speech, you must upload a consent recording .
The consent recording is a .wav or .mp3 file where the voice talent explicitly states that they consent to the creation of a synthetic version of their voice.
Audio samples and transcripts (training data) are uploaded later for training the model.
The profile itself requires the consent recording to comply with Microsoft's responsible AI principles.
Reference: Custom Neural Voice requirements

Entity linking in Azure AI Language identifies entities in text and links them to entries in a knowledge base such as Wikipedia, providing precisely the additional context and references you need. Custom NER extracts entities but doesn't link to Wikipedia; Content Safety moderates content; Key phrase extraction surfaces topics without external links.
Microsoft References:
* Entity linking overview (links entities to Wikipedia). Microsoft Learn
* How to call the Entity Linking API (returns entities with Wikipedia links)

You're indexing content with Azure AI Search and you want to recognize and retrieve properties from invoices (such as invoice number, vendor, dates, totals, line items). The Azure service designed for structured information extraction from forms and documents is Azure AI Document Intelligence (formerly Form Recognizer).
In an Azure AI Search enrichment pipeline, you add a skillset. To pull structured fields from invoices, include a skill that invokes Document Intelligence's prebuilt Invoice model. This model already extracts common invoice fields, so you don't have to train anything-meeting the goal of minimal effort. You can wire this in using the built-in Document Intelligence skill for Search, or as a custom Web API skill that calls the Document Intelligence REST API, and then map the extracted fields into your index for retrieval and filtering.
Why the others are not suitable:
* Azure OpenAI is for generative/LLM tasks, not schema-level invoice field extraction.
* Azure AI Immersive Reader improves reading accessibility; it doesn't extract business fields.
* Azure Custom Vision is for image classification/object detection, not form/key-value extraction.
Microsoft References (Azure AI Solution)
* Azure AI Search skillsets - add AI enrichment and custom skills (Web API skill).
* Document Intelligence skill for Azure AI Search (formerly Form Recognizer skill) - integrate extraction into indexing.
* Prebuilt Invoice model - fields extracted (vendor, invoice ID/date, totals, taxes, line items, etc.).
* Azure AI Document Intelligence overview - key-value extraction and table/line-item parsing from documents.

"Protected material detection" in Content Safety is designed to flag model outputs that match copyrighted text
/code (e.g., lyrics, articles, recipes, GitHub code). It is not used to tune or test content filter configurations for safety categories like hate/sexual/violence/self-harm. Therefore, it does not meet the requirement to optimize content filters by running tests on sample questions.