Microsoft · Free Practice Questions · Last reviewed May 2026
24real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
29% of exam · 6 sample questions below
A company stores customer names, addresses, and order history. They need to perform complex queries that join customer and order data. Which type of data store is most appropriate for this scenario?
Key-value store
Relational database
A relational database is the best fit because it stores customers and order history in separate, normalized tables linked by foreign keys, such as a customer ID. ANSI SQL supports JOIN operations to combine these tables on demand, so you can query a specific customer's details alongside all their past orders. Enforcing a defined schema and referential integrity ensures names, addresses, and order records remain consistent and accurate. This matches the structured, transactional nature of customer/order data.
Document database
Graph database
A retail company captures real-time sensor data from IoT devices to detect anomalies and predict equipment failures. The data must be processed immediately as it arrives. Which type of data processing workload best describes this scenario?
Batch processing
Streaming processing
Streaming processing is the correct choice because it ingests and analyzes data continuously as it arrives, rather than waiting for a complete dataset. For real-time IoT sensor feeds, services like Azure Stream Analytics can process event streams with sub-second latency, applying time-windowed aggregations, filters, and anomaly detection logic to trigger immediate alerts. This supports proactive failure prediction and operational monitoring, which is impossible with store-then-process approaches.
Online transaction processing (OLTP)
Data warehousing
Which classification of data describes information that has a fixed schema and is organized into rows and columns, such as data found in a relational database table?
Unstructured data
Semi-structured data
Structured data
Structured data is information that conforms to a fixed schema, typically represented in tables with defined columns and rows. This is the fundamental format used by relational database management systems, where each table has a predetermined set of attributes and data types. If the question describes information with an explicit, predefined structure, structured data is the correct classification.
Transformed data
A logistics company stores shipping waybill data as JSON documents. Each document contains fields like 'shipmentId', 'destination', and 'items', but the number of items and the fields within each item can vary between shipments. Which category best describes this type of data?
Operational data
Semi-structured data
JSON documents consist of key-value pairs, nested objects, and arrays, but each waybill may have a different set of fields—some optional, some nested. This self-describing format provides inherent organization through keys and hierarchical structure, yet it does not enforce a rigid, predefined schema. That combination of organizational properties without a fixed tabular schema is the defining characteristic of semi-structured data, which is why this is the correct classification.
Unstructured data
Structured data
A consulting firm collects client information in two forms: a spreadsheet with columns for Name, Address, and Phone Number, and audio recordings of client meetings. Which of the following statements correctly categorizes these data types?
Both the spreadsheet data and the audio recordings are examples of structured data.
The spreadsheet data is structured, and the audio recordings are semi-structured.
The spreadsheet data is structured, and the audio recordings are unstructured.
A spreadsheet is structured because it has a fixed schema: a defined set of columns, each with a consistent data type and rows that conform to that schema, allowing direct querying via SQL or similar tools. In contrast, audio recordings exist as continuous analog or digital signal streams with no inherent fields, keys, or column definitions. They cannot be directly indexed, searched, or queried without first applying preprocessing such as speech-to-text or audio feature extraction, which is the defining characteristic of unstructured data.
The spreadsheet data is semi-structured, and the audio recordings are unstructured.
A company operates an online store that processes customer orders. When a customer places an order, the system must immediately reduce the inventory count for the purchased items and record the order details. At the end of each month, the company runs reports that aggregate sales data over the past month to analyze trends. Which type of data processing workload best describes the order placement activity?
Transactional processing
Order placement is the archetypal OLTP workload: it demands immediate, atomic updates to both inventory and order tables, where a failure in any step rolls back the entire transaction. ACID properties (atomicity, consistency, isolation, durability) guarantee that stock levels never go negative and orders are never left half-recorded, even under concurrent customer requests. Unlike reporting or analytics, this is a low-latency, write-heavy operation that cannot tolerate deferred or inconsistent updates.
Analytical processing
Batch processing
Stream processing
Want more Describe core data concepts practice?
Practice this domain29% of exam · 6 sample questions below
A manufacturer collects sensor data from thousands of IoT devices every second. The data is ingested into Azure Event Hubs and then needs to be stored for historical analysis. The analytics team will run complex aggregations and time-series queries over petabytes of data, expecting fast results even with large scans. Which Azure service should be used as the analytical data store?
Azure Data Lake Storage Gen2
Azure SQL Database
Azure Synapse Analytics dedicated SQL pool
Azure Synapse Analytics dedicated SQL pool is a purpose-built enterprise data warehouse that uses massively parallel processing (MPP) to distribute tables across decoupled storage and compute nodes. Each node stores compressed columnar (columnstore) data, which minimizes I/O during large analytical scans and aggregations. The query optimizer breaks complex queries into parallel tasks operating across all nodes, so even petabyte-scale IoT telemetry can be joined, filtered, and aggregated efficiently. This architecture is exactly why it is the recommended service for large-scale analytical workloads in Azure.
Azure Cosmos DB
A manufacturing company has a streaming data pipeline that ingests sensor data from factory equipment into Azure Event Hubs. The data must be prepared for reporting by cleaning invalid records, removing duplicates, and aggregating readings into 5-minute windows. The transformed data needs to be stored in a columnar format in a data lake to support efficient querying by data analysts using SQL. Which Azure service should perform the data transformation and loading?
Azure Data Factory
Azure Databricks
Azure Stream Analytics
Azure Stream Analytics is a serverless real-time analytics service that can ingest data from Event Hubs, perform time-windowed aggregations, clean data, and output to Azure Data Lake Storage in the desired columnar format. It is the most straightforward and cost-effective choice for this streaming ETL scenario.
Azure Synapse Pipelines
A data analytics team stores sales transaction data in Parquet files in Azure Data Lake Storage Gen2. They want to run complex analytical queries that join this data with dimension tables stored in Azure Synapse Analytics dedicated SQL pool. The team prefers not to move or copy the data from the data lake. Which feature should they use to query the data lake data directly?
Azure Data Factory pipelines
PolyBase external tables
PolyBase external tables in Azure Synapse dedicated SQL pool use the T-SQL language to create an external table pointing at Parquet files in Azure Data Lake Storage, allowing instant querying without moving the underlying data. PolyBase performs schema inference and can push down filtering operations to the file format, so it is the native mechanism for reading file data directly from Synapse. This matches the requirement of querying stored transaction data in place.
Azure Stream Analytics
Azure Databricks notebooks
A healthcare analytics company receives continuous streams of patient monitoring data from IoT devices. The data must be processed in near real-time to detect critical events (e.g., abnormal heart rate). Processed data is then stored in a columnar format for historical analysis and reporting by data analysts using SQL. Which combination of Azure services should they use for ingestion, processing, and storage?
Azure Event Hubs, Azure Stream Analytics, Azure Synapse Analytics
Event Hubs is a fully managed, partitioned streaming ingestion service that can absorb millions of events per second, while Stream Analytics executes continuous SQL-like queries over tumbling, hopping, and sliding windows to detect patterns and transform data. Synapse Analytics then serves as the columnar data warehouse, using dedicated or serverless SQL pools to run historical T-SQL analytics at scale. This forms an integrated hot path because every layer is purpose-built for real-time and analytic workloads with no need for custom cluster management.
Azure IoT Hub, Azure Data Factory, Azure SQL Data Warehouse
Azure Event Hubs, Azure Stream Analytics, Azure Cosmos DB
Azure Blob Storage, Azure Databricks, Azure Table Storage
A retail chain collects daily sales data from hundreds of stores. The data is stored as CSV files in Azure Data Lake Storage Gen2. The analytics team needs to run complex SQL queries that join sales data with product dimensions and aggregate results across petabytes of data. Queries must return results within seconds. Which Azure service is best suited for this analytical workload?
Azure Synapse Analytics
Azure Synapse Analytics is correct because its massively parallel processing (MPP) architecture splits a single query across many compute nodes, each scanning a different partition of the data simultaneously, so petabyte-scale joins and aggregations can finish in seconds rather than minutes. It can read CSV files directly from Azure Data Lake Storage Gen2 using PolyBase external tables or CREATE EXTERNAL TABLE AS SELECT (CETAS), which means no pre-loading into a traditional relational store is needed. Features like row-level security, workload management, and result-set caching further make it a true cloud-scale analytical SQL engine.
Azure SQL Database
Azure Analysis Services
Azure HDInsight
A financial analytics company has petabytes of transaction data stored as Parquet files in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple tables and return results within seconds. The company wants to query the data directly without moving it to another store. Which Azure service should they use?
Azure SQL Database
Azure Synapse Serverless SQL pool
Azure Synapse Serverless SQL pool is purpose-built for on-demand T-SQL queries over data stored in Azure Data Lake Storage (ADLS) or other open formats like Parquet. It automatically scales compute resources to match the size and complexity of the query, eliminating capacity planning and enabling petabyte-scale transactional analytics without provisioning dedicated infrastructure. Because it reads data directly from the lake via a T-SQL endpoint, it provides a familiar relational interface while preserving the cost benefits of a serverless model.
Azure HDInsight
Azure Databricks
Want more Describe an analytics workload on Azure practice?
Practice this domain24% of exam · 6 sample questions below
A company is migrating an on-premises SQL Server database to Azure. They want to ensure that database administrators (DBAs) can perform administrative tasks but cannot view sensitive customer data in query results. Which Azure SQL feature should they implement?
Dynamic Data Masking
Always Encrypted
Always Encrypted is correct because it encrypts sensitive data client-side, meaning the plaintext values are never transmitted to or stored in the SQL database. The database engine and DBAs only see ciphertext; the column master key that would permit decryption is held outside the database, typically in Azure Key Vault or the client's key store. Without that key, even a DBA with full server privileges cannot read the original data, which directly satisfies the requirement.
Transparent Data Encryption
Row-Level Security
A software-as-a-service (SaaS) provider hosts a multi-tenant application with a separate database for each tenant. They anticipate scaling to thousands of tenants and want to minimize cost while allowing tenants to share resources flexibly. Which Azure SQL offering is most suitable?
Azure SQL Database elastic pool
Azure SQL Database elastic pools share a set of eDTUs or vCores across multiple databases, letting a SaaS provider assign per-database minimum and maximum performance limits. This design absorbs unpredictable usage spikes from different tenants without over-provisioning each database, making it the most cost-efficient and operationally simple choice for a large multi-tenant workload.
Azure SQL Database (single database)
Azure SQL Managed Instance
SQL Server on Azure Virtual Machine
A company runs an e-commerce application backed by an on-premises SQL Server database. They plan to migrate to Azure SQL Database and require automatic failover across two Azure regions for disaster recovery. The application must continue to connect using the same connection string after a failover, with no code changes. Which feature should they implement?
Active Geo-Replication
Elastic pools
Failover groups
Failover groups are the correct choice because they automatically replicate databases or elastic pools to a secondary region and provide an automatic failover mechanism that requires no application code changes. The group exposes a single readable/writable listener endpoint that remains identical after failover, so clients keep connecting to the same fully qualified domain name. You can also set a graceful data-loss boundary (RPO) and configure a read-only listener for offloading reporting traffic, making it a fully managed, PaaS-native DR solution.
SQL Server on Azure Virtual Machine with Always On Availability Groups
A company is migrating a legacy on-premises database to Azure. They require the ability to run cross-database queries within the same logical server, full control over database collation settings, and want to minimize management overhead for infrastructure patching. The database size is under 1 TB and they do not need instance-level features like SQL Agent jobs or linked servers. Which Azure SQL offering should they choose?
Azure SQL Database
Azure SQL Database is a PaaS service that handles patching, supports elastic query for cross-database queries, and allows collation settings on a per-database level. It does not include SQL Agent or linked servers, which are not required here.
Azure SQL Managed Instance
SQL Server on Azure Virtual Machine
Azure Synapse SQL pool
A company is migrating an on-premises SQL Server database to Azure. The database uses SQL Server Integration Services (SSIS) packages for daily ETL processes. The company wants to minimize administrative overhead for patching and backup management, but needs to retain full control over instance-level configurations and support for SSIS. Which Azure SQL service should they choose?
Azure SQL Database
Azure SQL Managed Instance
Azure SQL Managed Instance is the correct choice because it provides near 100% compatibility with on-premises SQL Server, including support for SQL Server Integration Services (SSIS) via Azure-SSIS Integration Runtime. As a Platform as a Service (PaaS) offering, it automates critical maintenance tasks such as patching, backups, and high availability, while preserving instance-scoped features like SQL Agent, linked servers, and CLR. This minimizes administrative overhead while supporting SSIS, making it the ideal target for a direct migration of a SQL Server database with integration services workloads.
Azure Synapse Analytics
Azure SQL Server on Azure Virtual Machines
A company has an existing on-premises SQL Server database that is 500 GB in size. The database uses SQL Server Agent jobs for scheduled maintenance and linked servers to query data from a remote SQL Server instance. The company wants to migrate to Azure with minimal application changes and needs automated backups and patching. Which Azure SQL service should they choose?
Azure SQL Database
Azure SQL Managed Instance
Azure SQL Managed Instance provides high compatibility with on-premises SQL Server, including support for SQL Agent jobs and linked servers. It also includes automated backups, patching, and high availability, meeting all requirements.
SQL Server on Azure Virtual Machines
Azure Database for PostgreSQL
Want more Identify considerations for relational data on Azure practice?
Practice this domain18% of exam · 6 sample questions below
A social media application stores user profile data as JSON documents. Each user's document has a different structure, with fields that vary based on user activity. The application needs to query these documents efficiently using SQL-like syntax and support high write throughput. Which Azure data store is most appropriate for this workload?
Azure SQL Database
Azure Blob Storage
Azure Cosmos DB
Azure Cosmos DB is a globally distributed, multi-model NoSQL database that natively stores JSON documents as first-class citizens. Its flexible schema allows user profiles with varying fields and nested structures to be inserted without migrations, while the SQL API provides rich, index-backed querying over nested JSON properties. With turnkey global distribution, tunable consistency, and guaranteed low-latency reads/writes, it is explicitly designed for social media workloads that demand both variable data shapes and high throughput at scale.
Azure Table Storage
A ride-sharing application needs to store real-time GPS location updates from drivers and passengers. The data is ingested as key-value pairs where the key is the user ID and the value is a timestamped location. The application requires low-latency reads and writes for millions of concurrent users, and the data model is simple with no need for complex queries or joins. Which Azure NoSQL database API should be used for this workload?
Azure Cosmos DB Table API
The Table API is designed for key-value storage with simple queries by partition key and row key, providing low-latency access at global scale. It is ideal for this type of high-throughput, simple data access pattern.
Azure Cosmos DB SQL (Core) API
Azure Cosmos DB for MongoDB API
Azure Cosmos DB for Apache Gremlin API
A global social media platform stores user profile images (JPEG) and activity logs in JSON format. The logs have varying structures based on the type of activity. The application requires low-latency reads of images from any region and the ability to query logs using SQL-like syntax. Which Azure data storage solution should they use for each data type?
Azure Table Storage for images and Azure Cosmos DB (Table API) for logs
Azure Blob Storage with a CDN for images and Azure Cosmos DB (SQL API) for logs
Azure Blob Storage is purpose-built for storing unstructured binary data like JPEG images, offering massive scalability and low cost per gigabyte. Pairing it with Azure CDN caches image copies at edge locations worldwide, dramatically reducing latency for global users. Azure Cosmos DB's SQL API stores each log entry as a JSON document and supports querying with familiar SQL-like syntax, accommodating the variable structure of the logs without requiring a predefined schema. This combination directly satisfies the requirements for unstructured image storage and flexible, queryable log storage.
Azure Files for images and Azure SQL Database for logs
Azure Disk Storage for images and Azure Cosmos DB (MongoDB API) for logs
A retail company stores product catalog data as JSON documents. Each product has a different set of attributes depending on its category (e.g., electronics have 'voltage', clothing has 'size'). The application needs to query products by category and price range efficiently. Which Azure data store is most appropriate for this workload?
Azure Cosmos DB
Azure Cosmos DB is a multi-model NoSQL database with native JSON support and schema-agnostic automatic indexing, allowing queries on any attribute such as category or price without predefined schema. Its low-latency index on every property makes it ideal for product catalogs where different items have varying attributes and customers filter by arbitrary combinations. Cosmos DB also offers predictable throughput scaling, ensuring consistent query performance as catalog size grows.
Azure SQL Database
Azure Blob Storage
Azure Table Storage
A media company stores large video files and associated metadata (title, duration, tags) as JSON documents. The application requires low-latency streaming of videos to users worldwide and the ability to quickly query metadata by tag. Which combination of Azure services should the company use?
Azure Blob Storage for videos and Azure Cosmos DB for metadata
Azure Blob Storage is purpose-built for large unstructured binary data: it offers high-throughput write/read, configurable access tiers, and HTTPS-based access suitable for storing and delivering video files at scale. Azure Cosmos DB complements this by storing video metadata as flexible JSON documents, with automatic indexing and sub-millisecond point reads that support rich queries on tags, durations, and upload dates. This pairing keeps the media payload and its searchable catalog decoupled, so storage optimization and query performance are each handled by the most appropriate service.
Azure Blob Storage for both videos and metadata
Azure Cosmos DB for videos and Azure Table Storage for metadata
Azure Files for videos and Azure SQL Database for metadata
A social media application stores user profiles as JSON documents. Each user profile can have different attributes (e.g., some have 'education', others have 'work experience'). The application needs to query profiles by any attribute with low latency. Which Azure data store is most appropriate?
Azure Blob Storage
Azure Table Storage
Azure Cosmos DB (SQL API)
Azure Cosmos DB (SQL API) is a purpose-built NoSQL document database that stores data natively as JSON. Its schema-agnostic model lets each user profile contain a different set of attributes without migrations, while automatic indexing on every property enables fast, attribute-level queries via SQL syntax. This directly matches the requirement for flexible JSON documents with varying structures.
Azure SQL Database
Want more Describe considerations for working with non-relational data on Azure practice?
Practice this domainThe DP-900 exam has 50 questions and must be completed in 60 minutes. The passing score is 700/1000.
Conceptual questions on data fundamentals, relational and non-relational data, analytics workloads, and Azure data services.
The exam covers 4 domains: Describe core data concepts, Describe an analytics workload on Azure, Identify considerations for relational data on Azure, Describe considerations for working with non-relational data on Azure. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Microsoft DP-900 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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