Microsoft - AI-901: Microsoft Azure AI Fundamentals (beta)

Sample Questions

Explanation:

The Azure AI Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. 

Azure AI Vision includes the following capabilities:

• Optical Character Recognition (OCR) The Optical Character Recognition (OCR) service extracts text from images. You can use the Read API to extract printed and handwritten text from photos and documents. It uses deep-learning-based models and works with text on various surfaces and backgrounds. These include business documents, invoices, receipts, posters, business cards, letters, and whiteboards. The OCR APIs support extracting printed text in several languages. Follow the OCR quickstart to get started.

• Image Analysis The Image Analysis service extracts many visual features from images, such as objects, faces, adult content, and auto-generated text descriptions. Follow the Image Analysis quickstart to get started.

• Face The Face service provides AI algorithms that detect, recognize, and analyze human faces in images. Facial recognition software is important in many different scenarios, such as identification, touchless access control, and face blurring for privacy. Follow the Face quickstart to get started.

Reference: What is Azure AI Vision?

Explanation:

The Document Intelligence receipt model combines powerful Optical Character Recognition (OCR) capabilities with deep learning models to analyze and extract key information from sales receipts. Receipts can be of various formats and quality including printed and handwritten receipts. The API extracts key information such as merchant name, merchant phone number, transaction date, tax, and transaction total and returns structured JSON data. Receipt model v4.0 (GA) supports other fields including ReceiptType, TaxDetails.NetAmount, TaxDetails.Description, TaxDetails.Rate and CountryRegion along with VAT table extraction on general hotel receipts.

Input requirements

The following file formats are supported.

• Photos and scans: For best results, provide one clear photo or high-quality scan per document.
• PDFs and TIFFs: For PDFs and TIFFs, up to 2,000 pages can be processed. (With a free-tier subscription, only the first two pages are processed.)
• File size: The file size for analyzing documents is 500 MB for the paid (S0) tier and 4 MB for the free (F0) tier.
• Image dimensions: The dimensions must be between 50 pixels x 50 pixels and 10,000 pixels x 10,000 pixels.
• Password locks: If your PDFs are password-locked, you must remove the lock before submission.
• Text height: The minimum height of the text to be extracted is 12 pixels for a 1024 x 768-pixel image. This dimension corresponds to about 8-point text at 150 dots per inch.
• Custom model training: The maximum number of pages for training data is 500 for the custom template model and 50,000 for the custom neural model.
• Custom extraction model training: The total size of training data is 50 MB for template model and 1 GB for the neural model.
• Custom classification model training: The total size of training data is 1 GB with a maximum of 10,000 pages. For 2024-11-30 (GA), the total size of training data is 2 GB with a maximum of 10,000 pages.
• Office file types (DOCX, XLSX, PPTX): The maximum string length limit is 8 million characters.

Reference: Document Intelligence receipt model

Explanation:

Key phrase extraction is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction returns the main topics: "food" and "wonderful staff."

Reference: What is key phrase extraction in Azure AI Language?

Explanation:

Utterances are inputs from users that your app needs to interpret. To train LUIS to extract intents and entities from these inputs, it's important to capture various different example utterances for each intent. Active learning, or the process of continuing to train on new utterances, is essential to the machine-learning intelligence that LUIS provides.

Collect utterances that you think users will enter. Include utterances, which mean the same thing but are constructed in various ways:

• Utterance length - short, medium, and long for your client-application
• Word and phrase length
• Word placement - entity at beginning, middle, and end of utterance
• Grammar
• Pluralization
• Stemming
• Noun and verb choice
• Punctuation - using both correct and incorrect grammar

Reference: Utterances

Explanation:

Azure Content Understanding analysis capabilities help you transform unstructured data into structured, machine-readable information. By precisely identifying and extracting elements while preserving their structural relationships, you can build powerful processing workflows for a wide range of applications.

The contents object with the kind document supports output for a range of different input files, including document, image, text, and structured files. You can use these outputs to extract meaningful content from your files, preserve document structures, and unlock the full potential of your data.

The document content kind includes output for input files like:

• Documents: PDFs, Word documents, PowerPoint presentations, and Excel spreadsheets
• Figures: Photos, scanned documents, charts, and diagrams
• Text files: Plain text, HTML, Markdown, and RTF
• Structured content: XML, JSON, CSV, and TSV files
• Email: EML and MSG message formats

The Content Understanding API returns analysis results in a structured JSON format.

Reference: Document analysis: Extract structured content