DP-900 Azure Data Fundamentals Exam Questions's

The process of collecting data from various sources for storage or processing.

Large and complex data sets that require advanced analytics and storage solutions.

Processing large volumes of data at scheduled intervals.

Real-time data processing as data arrives.

Data organized in fixed fields such as tables or spreadsheets.

Data without a predefined format such as images or videos.

Data with some structure but flexible schema, such as JSON or XML.

The graphical representation of data to find insights and patterns.

Data that describes other data.

Storing duplicate copies of data for reliability.

A design that defines data structure and relationships.

The management of data availability, usability, and security.

A set of processes to move and transform data between systems.

To organize and manage metadata about data assets.

Extract, Transform, Load — the process of moving and preparing data.

Extract, Load, Transform — transformation happens after loading data into storage.

Scalability, flexibility, and cost efficiency.

The time delay between data creation and availability for use.

The process of examining datasets to draw conclusions.

A database that stores data in tables with predefined relationships.

Structured Query Language used to manage and query relational databases.

A fully managed relational database service in Azure.

A unique identifier for each record in a table.

A field linking one table to another.

The process of reducing data redundancy and improving data integrity.

Combining tables to improve read performance.

A virtual table created from a query on one or more tables.

A precompiled SQL code that can be executed repeatedly.

A structure that improves data retrieval speed.

A managed SQL Server instance with near 100% compatibility.

A managed PostgreSQL service on Azure.

A managed MySQL database service on Azure.

Atomicity, Consistency, Isolation, Durability — ensure reliable transactions.

A unit of work performed against a database.

Maintaining accuracy and consistency of data.

A command to combine rows from two or more tables.

An index that determines the physical order of data in a table.

An index that stores data separately from the table.

Ensures relationships between tables remain consistent.

A rule that defines valid data values in a table.

Ensures all values in a column are different.

A distributed data warehouse in Azure Synapse Analytics.

Microsoft’s proprietary extension of SQL for Azure SQL and SQL Server.

Splitting a database into smaller parts for scalability.

Dividing data into segments to improve performance and manageability.

Copying data between systems to ensure availability and durability.

A SQL object that executes automatically in response to events.

A stored program that returns a value.

A function that returns a single value.

A function that returns a table.

How long data is stored before being deleted or archived.

Automatic scaling of resources based on workload.

A detailed breakdown of how SQL executes a query.

A SQL database engine optimized for IoT and edge devices.

A mechanism for automatic database failover across regions.

Reusing existing database connections to improve performance.

Data that tracks historical changes automatically.

A technology in Synapse that allows querying external data using T-SQL.

The process of improving query performance.

A type of database designed for unstructured or semi-structured data.

A globally distributed multi-model NoSQL database service.

Interfaces like SQL, MongoDB, Cassandra, Gremlin, and Table.

A field that determines data distribution across partitions.

The amount of data operations per second measured in Request Units (RUs).

Defines how up-to-date or synchronized replicas are in a database.

Strong, Bounded Staleness, Session, Consistent Prefix, Eventual.

Updates are propagated gradually and may be temporarily inconsistent.

Ensures all reads return the most recent committed version.

A NoSQL key-value store for semi-structured data.

A NoSQL database that stores data in JSON-like documents.

A database where data is stored as key and value pairs.

A database that stores data in columns instead of rows.

A NoSQL database optimized for relationships and networks.

Storing unstructured data like text, images, and video.

A logical grouping of blobs, like a folder.

A storage model that manages data as objects.

A scalable storage optimized for big data analytics.

Organizes data in folders and directories for efficient access.

A managed Apache Hadoop and Spark service for big data analytics.

A fast, collaborative Apache Spark-based analytics platform.

Resilient Distributed Dataset, a core concept in Spark.

A distributed collection of data organized into named columns.

An open-source storage layer that brings ACID transactions to data lakes.

Splitting data across multiple servers for scalability.

Copying data across regions for fault tolerance.

Automatically adjusts throughput based on workload.

A log of changes that occur in the database containers.

A measure of performance in Request Units per second (RU/s).

Distributing data across regions for global availability.

Time to Live — automatically deletes items after a set time.

A columnar store for running analytics without impacting OLTP workloads.

A service for storing and retrieving messages asynchronously.

Storage access levels — hot, cool, and archive.

A fully managed file share in the cloud.

Allows Azure Table apps to use Cosmos DB backend.

A lightweight format for storing and exchanging data.

A binary JSON format used by MongoDB.

Writes become consistent after some time delay.

States that a distributed system can only guarantee two of Consistency, Availability, Partition tolerance.

An integrated analytics service combining big data and data warehousing.

A cloud-based ETL service to orchestrate and automate data workflows.

A business analytics tool for visualizing and sharing insights.

A cloud service to build, train, and deploy machine learning models.

The process of converting data into a usable format.

Cleaning and preparing data for analysis.

A fully managed platform for building semantic data models.

A centralized repository for storing raw data of all types.

Data lake stores raw data; data warehouse stores structured, processed data.

Online Transaction Processing — optimized for managing transactions.

Online Analytical Processing — optimized for querying and reporting.

A fast and highly scalable data exploration service.

Rules that define how long data is stored.

An event that starts a data pipeline in Azure Data Factory.

Connection information for data sources.

Represents data structures within a linked service.

A processing step within a data pipeline.

A compute infrastructure used by ADF to move and transform data.

A visual way to design and run data transformations in ADF.

A service to collect and analyze performance data from Azure resources.

A tool to query and analyze log data collected by Azure Monitor.

A unified data governance and catalog service.

A Windows application for creating Power BI reports.

An online SaaS platform to share and collaborate on Power BI reports.

A tool to connect on-premises data to Power BI cloud services.

A formula language used for data analysis expressions.

A calculation used for analysis in reports.

The process of updating data from source systems.

A data streaming platform for ingesting and processing event data.

A service for analyzing streaming data in real-time.

A data connection technology for discovering, connecting, and combining data.

A shared environment to collaborate on reports and dashboards.

Restricting data access for users at the row level.

Updating only changed data instead of reloading all data.

Combines multiple data sources in a single Power BI model.

A service to securely share data between organizations.

A tool to manage Azure Storage data from desktop.

Tracking the execution and performance of data pipelines.

Tracking the flow of data from source to destination.

A centralized log collection and analysis platform.

An on-demand query service in Azure Synapse Analytics.

A provisioned data warehouse for high-performance analytics.

A service for automating workflows and integrations.

A tool for automating repetitive business tasks.

A service that provides best practice recommendations for Azure resources.

A logical container for Azure resources.

A cloud service to securely store secrets, keys, and certificates.

A service to register, discover, and manage data assets.

A tool to monitor and control Azure spending.

A geographical area containing Azure datacenters.

Physically separate datacenters within a region for resilience.