Term 31
Cloud logging
Cloud logging is the practice of collecting, storing, and analyzing log data generated by cloud-based resources and applications to monitor performance, troubleshoot issues, and maintain security.
Acronym study
Terms 31–60 of 150 AI-900 acronyms and key terms. Each entry includes a plain-English definition and a link to the full 800-word glossary page with exam context and practice questions.
Term 31
Cloud logging is the practice of collecting, storing, and analyzing log data generated by cloud-based resources and applications to monitor performance, troubleshoot issues, and maintain security.
Term 32
Cloud monitoring is the process of observing, measuring, and managing an organization's cloud infrastructure and applications to ensure performance, availability, and security.
Term 33
A CloudFormation Stack is a collection of AWS resources that are created, managed, and deleted together as a single unit using a template.
Term 34
A CloudFormation StackSet is a feature that lets you deploy and manage a collection of stacks across multiple AWS accounts and Regions from a single template.
Term 35
Clustering is a technique where multiple servers work together as a single system to keep applications running even if one server fails.
Term 36
A cognitive skill is the ability of an AI system to interpret, analyze, and respond to data in a way that mimics human thought processes, enabling tasks like understanding language, recognizing images, and making decisions.
Term 37
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Term 38
Concurrency is the ability of a system to handle multiple tasks at the same time by dividing resources and switching between them efficiently.
Term 39
A confidence score is a number (often between 0 and 1 or 0 and 100%) that tells you how likely it is that an AI model's prediction or answer is correct.
Term 40
A content filter is an AI-powered safety system that screens user prompts and AI-generated responses for harmful, offensive, or restricted content, helping to ensure responsible use of Azure AI services.
Term 41
Conversational language understanding is an Azure AI service that helps applications interpret natural human language in conversations or text inputs.
Term 42
Copilot is a set of AI-powered assistants from Microsoft that help users work more efficiently by generating text, answering questions, summarizing content, and automating tasks across applications like Windows, web browsers, and Microsoft 365.
Term 43
Cross-Region Replication is the automated copying of data from a storage bucket in one geographic region to a bucket in a different geographic region for disaster recovery, compliance, or lower latency access.
Term 44
A Dedicated Host is a physical server in the cloud that is reserved exclusively for your use, giving you control over which instances run on that host and visibility into the underlying hardware.
Term 45
Deep learning is a subset of machine learning that uses multi-layered neural networks to automatically learn patterns from large amounts of data.
Term 46
DNS enumeration is the process of systematically querying a Domain Name System (DNS) server to gather information about a target domain, including its subdomains, IP addresses, and mail server records.
Term 47
Document intelligence is a cloud-based service that uses artificial intelligence to extract, analyze, and understand information from documents like forms, invoices, and receipts automatically.
Term 48
EC2 Image Builder is a managed AWS service that simplifies the creation, customization, validation, and distribution of virtual machine images (Amazon Machine Images or AMIs) for use with EC2 instances.
Term 49
Amazon Elastic Container Registry (ECR) is a fully managed Docker container registry that stores, manages, and deploys container images securely.
Term 50
An Elastic Load Balancer automatically distributes incoming application or network traffic across multiple targets, such as Amazon EC2 instances, containers, or IP addresses, in one or more Availability Zones.
Term 51
Embedding is the process of converting high-dimensional data like text or images into a lower-dimensional numerical vector that captures semantic meaning for use in machine learning models.
Term 52
Entity extraction is the process of automatically identifying and classifying named entities in text, such as people, organizations, locations, dates, and technical terms, turning unstructured data into structured information.
Term 53
Enumeration is the systematic process of extracting detailed information about a target system, such as user accounts, network shares, services, and configurations, used during the reconnaissance phase of a security assessment.
Term 54
Error Reporting is the automated process of capturing, logging, and notifying relevant systems or personnel about errors that occur in software, hardware, or network components to facilitate diagnosis and resolution.
Term 55
Face detection is an AI service that identifies and locates human faces in images or video, distinguishing them from other objects or backgrounds.
Term 56
Failover routing is a network design that automatically redirects traffic to a backup path when the primary path fails, keeping services available.
Term 57
Fairness in AI means designing and deploying machine learning models that do not produce biased outcomes against any group of people based on protected characteristics like race, gender, or age.
Term 58
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Term 59
Fingerprinting is the process of gathering information about a target system or network to identify its operating system, services, software versions, and configuration details during the reconnaissance phase of a security assessment.
Term 60
A cloud-based service that uses machine learning to extract text, key-value pairs, and tables from documents like invoices and receipts.