Reinforce DP-900 concepts with active-recall study cards covering all 4 blueprint domains. Each card shows the question on the front and the correct answer with a full explanation on the back.
Flashcards work through active recall — the process of retrieving information from memory rather than passively re-reading it. Research consistently shows that active recall produces stronger, longer-lasting memory than re-reading study guides. For DP-900 preparation, this means flashcards are one of the highest-return study tools available.
Attempt recall first
Read the DP-900 question on each card, pause, and attempt to formulate the answer in your own words before revealing. This retrieval attempt — even if wrong — dramatically strengthens memory compared to immediately reading the answer.
Review wrong cards again
When you get a card wrong, note it and add it back to your review pile. Spaced repetition — seeing difficult cards more frequently — is the mechanism that makes flashcard study far more efficient than linear reading.
Study by domain
Group your DP-900 flashcard sessions by domain for the first 3–4 weeks. Master one domain before moving to the next. In the final week, shuffle all cards together to test cross-domain recall — which is what the real DP-900 exam requires.
Short sessions beat marathon reviews
20–30 flashcard cards per session, done daily, produces better retention than a single 200-card marathon session. Five short daily sessions per week over 4 weeks gives you over 400 total card reviews — enough to reliably pass DP-900.
Sample cards from the DP-900 flashcard bank. Read the question, think of the answer, then read the explanation 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?
Relational database
A relational database (e.g., Azure SQL Database) is most appropriate because the scenario requires joining customer and order data via complex queries. Relational databases enforce a fixed schema with tables, primary keys, and foreign keys, enabling efficient JOIN operations using SQL. This structure ensures data integrity and supports ACID transactions, which are essential for accurate order history and customer records.
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 Synapse Analytics dedicated SQL pool
Azure Synapse Analytics dedicated SQL pool is the correct choice because it is a massively parallel processing (MPP) engine designed for petabyte-scale data warehousing. It can run complex aggregations and time-series queries with fast results by distributing data across 60 distributions and using columnstore indexes for high compression and scan efficiency.
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?
Always Encrypted
Always Encrypted ensures that sensitive data is encrypted at all times, including during query processing, and that the encryption keys are never revealed to the database engine. This allows DBAs to perform administrative tasks (e.g., backups, index maintenance) while being unable to view the plaintext data in query results, because the decryption occurs only on the client side.
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 Cosmos DB
Azure Cosmos DB is the most appropriate choice because it natively supports storing and querying JSON documents with varying schemas, offers SQL-like query syntax via its core (SQL) API, and provides guaranteed low-latency reads/writes at any scale with automatic indexing of all fields. Its multi-model nature and configurable consistency levels make it ideal for high-throughput workloads like a social media application.
An e-commerce application processes customer orders. When an order is placed, the system must decrement the inventory count and process the payment. The application ensures that either both operations complete successfully or both are rolled back if any error occurs. Which database property does this guarantee?
Atomicity
Atomicity ensures that a transaction is treated as a single, indivisible unit of work: either all operations within it (decrement inventory and process payment) complete successfully, or none are applied. If any part fails, the database rolls back all changes, maintaining the 'all-or-nothing' guarantee. This is the core property described in the scenario.
A company uses Azure SQL Database for an order management system. The Orders table has columns: OrderID (int, primary key), CustomerID (int), OrderDate (datetime), Status (varchar), TotalAmount (decimal). Queries frequently filter on CustomerID and OrderDate to find orders from a specific customer within a date range. Which index would most improve performance for these queries?
A non-clustered index on (CustomerID, OrderDate) INCLUDE (TotalAmount)
The query filters on CustomerID and OrderDate, so a composite non-clustered index on (CustomerID, OrderDate) allows SQL Server to perform an index seek on both columns, drastically reducing the number of rows scanned. Including TotalAmount as a non-key column makes this a covering index, meaning all needed data (including TotalAmount) is in the index leaf pages, avoiding costly key lookups to the clustered index.
A gaming company stores player scores in Azure Cosmos DB using the NoSQL API. Each document contains fields: PlayerID (unique to the player), GameID, Score, Timestamp. The most common query is: 'Retrieve all scores for a specific GameID, ordered by Score descending.' Which property should be chosen as the partition key to minimize Request Unit (RU) consumption?
GameID
GameID is the correct partition key because the most common query filters on GameID, and Cosmos DB routes queries to the exact physical partition(s) containing that GameID's data. This minimizes RU consumption by avoiding cross-partition fan-out, as the query engine can target a single partition. Using any other field would force scanning multiple partitions, increasing RU cost.
A gaming company stores player profiles as JSON documents. Each profile includes standard fields like playerId, username, and email, as well as optional fields such as achievements, gamePreferences, and friendsList. The application needs to look up profiles by playerId with low latency (under 10 ms) and also run SQL-like queries to find players who have a specific achievement. Which Azure Cosmos DB API should they choose?
D. SQL (Core) API
The SQL (Core) API is the correct choice because it natively supports JSON documents with flexible schemas (including optional fields like achievements) and provides low-latency point reads by playerId (partition key) under 10 ms. It also enables SQL-like queries (e.g., SELECT * FROM c WHERE ARRAY_CONTAINS(c.achievements, 'specificAchievement')) to find players with a specific achievement, which aligns directly with the requirement.
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?
Always Encrypted
Always Encrypted ensures that sensitive data is encrypted at all times, including during query processing, and that the encryption keys are never revealed to the database engine. This allows DBAs to perform administrative tasks (e.g., backups, index maintenance) while being unable to view the plaintext data in query results, because the decryption occurs only on the client side.
A global e-commerce company uses Azure SQL Database for its product catalog. The application experiences high read traffic for product detail pages, often running the same queries for popular items. The database’s write workload is moderate. The company wants to improve read performance without increasing the cost of the primary database tier and without changing the application code. Which Azure SQL Database feature should they implement?
Read scale-out
Read scale-out (A) is correct because it offloads read-only queries to a secondary replica of the Azure SQL Database without changing the application code. By setting `ApplicationIntent=ReadOnly` in the connection string, the database routes read queries to a read-only replica, improving performance for high-read workloads like product detail pages while keeping the primary tier unchanged and avoiding additional cost for a higher tier.
A global e-commerce company uses Azure SQL Database for its product catalog. The database is hosted in the West US region. To ensure the catalog remains available if West US experiences an outage, the company wants to configure a secondary database in East US that can be used for reads and can be automatically promoted to primary during a disaster. They require a Recovery Point Objective (RPO) of less than 5 seconds and a Recovery Time Objective (RTO) of less than 30 minutes. Which feature should they implement?
Auto-failover groups
Auto-failover groups (Option B) are the correct choice because they provide automatic, orchestrated failover of a primary Azure SQL Database to a secondary region (East US) during an outage, meeting the RPO of less than 5 seconds (typically 5–10 seconds for active geo-replication) and RTO of less than 30 minutes (usually under 1 hour). The secondary database can be used for read-only queries, and the failover group ensures the entire group of databases fails over as a unit, maintaining the same connection string.
A manufacturing company stores IoT sensor data as JSON documents in Azure Cosmos DB. Each document has fields: deviceId (high cardinality, many unique values), timestamp, temperature, and humidity. The most frequent query is: 'Retrieve all readings for a specific deviceId from the last hour.' To minimize Request Unit (RU) consumption, which combination of partition key and indexing policy should be chosen?
Partition key: deviceId, Indexing: automatic on all properties
Option A is correct because deviceId is the most frequently filtered attribute (in the WHERE clause), making it an ideal partition key that ensures queries are scoped to a single physical partition, minimizing cross-partition fan-out. Automatic indexing on all properties allows efficient filtering on timestamp within the partition, while the index on deviceId is not strictly needed since the partition key itself routes the query, but it does not harm RU consumption significantly. This combination balances query performance and RU cost for the described workload.
A social media application stores user posts in Azure Cosmos DB using the NoSQL API. Each document includes: PostID (unique), UserID, Timestamp, Content. The most common query is: 'Get all posts for a specific UserID, sorted by Timestamp descending.' Which partition key should be chosen to distribute load evenly across physical partitions while also supporting this query efficiently?
UserID
UserID is the correct partition key because it evenly distributes write operations across physical partitions (each user has a unique ID) and directly supports the most common query: filtering by UserID. With UserID as the partition key, the query 'Get all posts for a specific UserID, sorted by Timestamp descending' becomes a single-partition query (using the partition key in the WHERE clause), which is efficient and avoids cross-partition fan-out. This design also allows Cosmos DB to use the Timestamp field as a sort key within each logical partition, enabling efficient sorting without additional indexing overhead.
A company uses Azure SQL Database for a financial system. The Transactions table contains millions of rows with a TransactionDate column. Queries frequently aggregate sales totals for the current month, but historical data must be retained for 7 years. Currently, queries scan the entire table, causing performance issues. The company also wants to simplify archiving of old data. Which design should they implement?
Implement table partitioning by month on TransactionDate.
Table partitioning by month on TransactionDate allows Azure SQL Database to efficiently manage and query large tables by splitting data into manageable segments. Queries that filter on TransactionDate for the current month will only scan the relevant partition(s), eliminating full table scans. Additionally, partitioning simplifies archiving by enabling swift partition switching to move old data to archive tables without complex ETL processes.
A retail company wants to analyze customer clickstream data in real-time to detect patterns and trigger personalized offers. They also store the raw clickstream data in Azure Data Lake Storage for later batch analysis. Which Azure service should they use for the real-time processing component?
Azure Stream Analytics
Azure Stream Analytics is the correct choice because it is designed for real-time data processing and analytics on streaming data, such as clickstream events. It can ingest data from sources like Azure Event Hubs, apply SQL-like queries to detect patterns, and output results to triggers or storage, all with sub-second latency. This matches the requirement for real-time pattern detection and personalized offer triggering.
A smart building company stores IoT sensor data in Azure Cosmos DB using the NoSQL API. Each document contains fields: deviceId (partition key), timestamp, temperature, and humidity. The most common query is to retrieve all readings for a specific device within a time range, which runs efficiently. However, the analytics team occasionally runs a query to find all devices that reported a temperature above 50 degrees Celsius in the last hour, without specifying deviceId. This query is very slow and consumes a high number of request units (RUs). What is the most likely reason for the slow performance and high RU consumption?
The query does not use the partition key, causing a cross-partition scan.
The query does not include the partition key (deviceId) in the filter, so Azure Cosmos DB cannot route it to a single physical partition. Instead, it must fan out the query to every partition, scanning all documents across the container. This cross-partition query consumes significantly more RUs and takes longer because each partition must be queried sequentially or in parallel, and the results are merged server-side.
A social media company stores user-generated posts as JSON documents. Each post contains fields such as postId, userId, timestamp, and content. The application needs to query posts by userId and timestamp ranges with low latency, and also perform SQL-like queries across all posts. The data volume is growing rapidly and must scale globally. Which Azure data store should the company use?
B) Azure Cosmos DB SQL API
Azure Cosmos DB SQL API is the correct choice because it provides native support for querying JSON documents with low-latency, including indexed queries on fields like userId and timestamp. Its global distribution capability ensures data can be replicated across multiple Azure regions for low-latency access worldwide, while its SQL API allows SQL-like queries across all posts, meeting both requirements.
A smart building company stores sensor data from thousands of IoT devices as JSON documents in Azure Cosmos DB using the NoSQL API. Each document contains fields: deviceId (string), timestamp (datetime), temperature (float), humidity (float), and additional device-specific fields (e.g., motionDetected, CO2level). The most common query is: SELECT * FROM c WHERE c.deviceId = 'sensor-123' AND c.timestamp >= '2025-01-01' AND c.timestamp < '2025-02-01' ORDER BY c.timestamp DESC. Which indexing strategy will provide the best performance for this query?
Create a composite index on (deviceId ASC, timestamp DESC)
Option B is correct because the query filters on `deviceId` (equality) and `timestamp` (range with ORDER BY DESC). A composite index on `(deviceId ASC, timestamp DESC)` allows Cosmos DB to efficiently locate the partition for the device and then scan the timestamp range in descending order without an in-memory sort, minimizing RU consumption and latency.
A startup is developing a web application that requires a relational database with PostgreSQL compatibility. They want a fully managed service that automatically handles backups, patching, and provides high availability with a 99.99% SLA. Which Azure service should they choose?
Azure Database for PostgreSQL
Azure Database for PostgreSQL is the correct choice because it is a fully managed relational database service that offers PostgreSQL compatibility, automatic backups, patching, and high availability with a 99.99% SLA. This meets the startup's requirements for a managed PostgreSQL solution without the need for manual administration.
A small online retailer wants to migrate its single on-premises SQL Server database to Azure. They require a fully managed relational database service with built-in high availability, automated backups, and no need to manage virtual machines. They do not need features like multiple databases with cross-database queries or SQL Agent. Which Azure service should they choose?
Azure SQL Database
Azure SQL Database is a fully managed Platform-as-a-Service (PaaS) relational database that provides built-in high availability (99.99% SLA with zone-redundant configuration), automated backups with point-in-time restore, and eliminates the need to manage virtual machines or operating system patches. It is the ideal choice for a single database migration when features like cross-database queries and SQL Agent are not required.
A software company develops a multi-tenant SaaS application. They deploy a separate Azure SQL Database for each tenant. The databases are small (2-5 GB) and have highly variable loads — some tenants use the app heavily during the day, others at night. The company wants to maximize resource utilization and minimize costs by allowing databases to share a pool of resources, while still maintaining a predictable performance per database. Which Azure SQL Database deployment option should they choose?
Elastic pool
C is correct because an elastic pool allows multiple Azure SQL databases with variable and unpredictable usage patterns to share a fixed pool of resources (eDTUs or vCores), maximizing resource utilization and minimizing cost. The pool provides a predictable performance per database through per-database min/max resource limits, which is ideal for the described multi-tenant SaaS scenario with small databases and highly variable loads.
A company is migrating an on-premises SQL Server database to Azure SQL Managed Instance. The database has a large fact table that is partitioned by date (monthly partitions) to improve query performance and simplify data archiving. The company wants to maintain the same partitioning strategy in Azure to avoid rewriting queries. Which feature in Azure SQL Managed Instance should they use to achieve this?
Table partitioning with partition functions and schemes
Azure SQL Managed Instance supports table partitioning using partition functions and partition schemes, which is the same feature available in SQL Server. This allows you to define monthly partitions on the fact table using a date column, preserving the existing partitioning strategy and query logic without modification. The partition function maps rows to partitions based on the date boundary values, and the partition scheme assigns those partitions to filegroups.
The DP-900 flashcard bank covers all 4 official blueprint domains published by Microsoft. Cards are distributed proportionally, so domains with higher exam weight have more cards.
Domain Coverage
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
Both flashcards and practice questions are evidence-based study tools. The difference is in what they train:
Flashcards — concept retention
Best for memorising definitions, acronyms, protocol behaviours, command syntax, and conceptual distinctions. Use flashcards to build the foundational vocabulary that DP-900 questions assume you know.
Best in: weeks 1–3
Practice tests — application
Best for applying concepts to realistic scenarios, eliminating distractors, and building exam stamina.DP-900 questions test scenario reasoning — not just recall — so practice tests are essential.
Best in: weeks 3–6
The most effective DP-900 study plan combines both: use flashcards for the first 2–3 weeks to build conceptual foundations, then shift to practice tests and mock exams in the final 2–3 weeks to apply and benchmark that knowledge. Most candidates who pass on their first attempt use both tools.
Yes. Courseiva provides free DP-900 flashcards across all official exam domains. Every card includes the correct answer and a full explanation of why it is right and why the distractors are wrong. The platform also includes topic-based practice, mock exams, and readiness tracking — no account required.
Courseiva has 982+ original DP-900 flashcards across all 4 exam blueprint domains. New cards are added regularly as the question bank grows. All cards are written by certified engineers against the official Microsoft exam objectives.
Courseiva flashcards are purpose-built for IT certification exams. Unlike generic flashcard platforms where content quality varies, every Courseiva card is mapped to the official DP-900 exam blueprint, written by engineers who hold the certification, and includes a full explanation of the correct answer and why the distractors are wrong. This explanation quality is what separates genuine learning from rote memorisation.
Courseiva is a web platform — an internet connection is required. For offline study, we recommend creating free Courseiva account, using the platform in your browser, and using your device's offline capabilities if your browser supports offline web apps.
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