Term 1
Alerting policy
An alerting policy is a set of rules that defines when to send notifications about a system condition that needs attention.
Acronym study
Terms 1–30 of 136 DP-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 1
An alerting policy is a set of rules that defines when to send notifications about a system condition that needs attention.
Term 2
AlloyDB is a fully managed, PostgreSQL-compatible database service from Google Cloud designed for high performance, scalability, and reliability for transactional and analytical workloads.
Term 3
Amazon Elastic File System (EFS) is a scalable, fully managed cloud file storage service that can be accessed by multiple Amazon EC2 instances concurrently using the NFS protocol.
Term 4
Amazon FSx is a fully managed service that makes it easy to launch, run, and scale feature-rich, high-performance file systems in the cloud, supporting popular file systems like Windows File Server and Lustre.
Term 5
Analytical data is information that has been cleaned, structured, and optimized for querying and reporting to support business decision-making.
Term 6
Artifact Registry is a managed service for storing, managing, and securing container images and other software packages in a centralized repository.
Term 7
Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora that automatically starts, scales, and stops database capacity based on your application's needs.
Term 8
AWS Backup is a fully managed service that centralizes and automates data backups across multiple AWS services, enabling you to define backup policies, monitor activity, and restore data from a single dashboard.
Term 9
Azure Cosmos DB is a fully managed, globally distributed NoSQL database service that offers fast reads and writes anywhere in the world with automatic scaling and multiple consistency models.
Term 10
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Term 11
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Term 12
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Term 13
Azure Storage is Microsoft's cloud-based service for storing data like files, messages, and backups with high durability and scalability.
Term 14
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Term 15
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Term 16
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
Term 17
Big data refers to extremely large and complex datasets that traditional data processing tools cannot handle efficiently, requiring specialized technologies to store, process, and analyze them.
Term 18
BigQuery ML is a feature in Google Cloud that lets you create and run machine learning models using standard SQL queries directly on data stored in BigQuery.
Term 19
BigQuery slots are units of computational capacity that determine how much processing power your queries can use in Google BigQuery.
Term 20
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
Term 21
Cloud Audit Logs are a record of actions taken by users, services, and resources inside a cloud environment, capturing who did what, when, and from where.
Term 22
Cloud Build is a managed service that compiles source code into deployable artifacts, often used in continuous integration and continuous delivery pipelines.
Term 23
Cloud KMS (Key Management Service) is a cloud-based service that lets you create, manage, and use encryption keys to protect your data at rest and in transit.
Term 24
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 25
Cloud monitoring is the process of observing, measuring, and managing an organization's cloud infrastructure and applications to ensure performance, availability, and security.
Term 26
Cloud Spanner is a fully managed, globally distributed relational database service from Google Cloud that combines the benefits of relational database structure with horizontal scalability and strong consistency.
Term 27
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
Term 28
Cloud Storage classes are categories of data storage services offered by cloud providers that differ in performance, availability, cost, and access frequency, allowing users to optimize costs based on how often data is accessed.
Term 29
A column is a vertical set of values in a database table that stores one specific type of attribute for every row.
Term 30
Consistency level is a setting in Azure data services that determines how quickly and accurately data is synchronized across multiple copies in a distributed system.