Azure AI Fundamentals (AI-901) flashcards
Azure AI Fundamentals (AI-901) · Build recall with a focused review session.
31 cards. Read the term and the definition here. The deck at the top of this page is optional.
- Machine learning
- A branch of AI in which a model learns patterns and relationships from data rather than following explicitly programmed rules, then generalizes to new inputs.
- Supervised learning
- Training with labeled examples, where each input is paired with the correct answer, so the model learns to predict that answer for unseen inputs.
- Unsupervised learning
- Training on data without labels, letting the algorithm discover structure such as clusters or unusual outliers on its own.
- Regression versus classification
- Regression predicts a continuous numeric value such as price or temperature; classification predicts a discrete category such as fraud or not fraud.
- Clustering
- An unsupervised technique that groups items so members of a group are more similar to each other than to items in other groups, without predefined labels.
- Reinforcement learning
- An approach where an agent learns a policy by taking actions in an environment and receiving rewards or penalties that shape future behavior.
- Object detection
- A computer vision task that locates multiple objects in an image by returning bounding boxes plus a class label and confidence for each detected object.
- Semantic segmentation
- A vision task that classifies every pixel of an image into a category, producing a mask that shows the exact shape of each region.
- Optical character recognition
- A vision capability that extracts machine-readable text from images or scanned documents, converting pixels of writing into editable strings.
- Natural language processing
- The AI field concerned with analyzing and generating human language, including sentiment analysis, entity extraction, translation, and summarization.
- Named entity recognition
- An NLP task that finds and labels specific entities in text, such as people, organizations, locations, dates, and quantities.
- Generative AI
- Models that create new content such as text, images, or code in response to a prompt, based on patterns learned from large training corpora.
- Large language model
- A generative model trained on very large text collections to predict tokens, enabling chat, summarization, question answering, and code generation.
- Hallucination
- A generative model output that is fluent but factually unsupported or fabricated, often caused by missing grounding or ambiguous prompts.
- Responsible AI principles
- Six Microsoft principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, applied across the AI lifecycle.
- Anomaly detection
- An unsupervised or semi-supervised technique that identifies data points or events that deviate significantly from expected or learned normal patterns.
- Microsoft Foundry
- Microsoft's platform for building, evaluating, and deploying AI applications and agents, organized as a resource hosting projects with models and connections.
- Foundry project
- A workspace inside a Foundry resource that contains model deployments, datasets, connections, agents, and evaluation runs for one application.
- Model catalog
- A browsable library in Foundry of foundation models from Microsoft and partners, each with descriptions, capabilities, and deployment options.
- Model deployment
- A hosted instance of a model that exposes an endpoint and credentials; client code targets the deployment name rather than the model family.
- Standard deployment
- A pay-per-token hosting option where consumption is billed by input and output tokens, with regional processing and no reserved capacity.
- Provisioned throughput
- A deployment option that reserves model capacity, giving predictable latency and steady cost instead of per-token billing.
- Serverless API deployment
- A deployment where models are hosted by Microsoft and called through an endpoint with key-based or identity-based authentication, without managing infrastructure.
- System message
- The prompt section that sets the model's role, tone, and constraints, applied before user messages and guiding all subsequent responses.
- Few-shot prompting
- Including a small number of example input-output pairs in the prompt so the model imitates the desired format and reasoning pattern.
- Retrieval-augmented generation
- A pattern that retrieves relevant chunks from your own data and inserts them into the prompt, grounding model responses in supplied evidence.
- Azure AI Search
- A cloud search service with vector and keyword indexing that is commonly used as the knowledge source for retrieval-augmented generation in Foundry.
- Foundry agent
- A configurable AI assistant in Foundry that combines a model deployment with instructions, tools, and knowledge sources to complete multi-step tasks.
- Groundedness metric
- An evaluation measure of how well a generated response is supported by the supplied source or context, rather than invented by the model.
- Relevance metric
- An evaluation measure of how directly a generated response addresses the user's request, independent of whether it is factually correct.
- Content safety filters
- Configurable controls that detect harmful categories such as violence or hate at set severity thresholds and block or flag model inputs and outputs.