DP-900 Practice Test 9 — 10 Questions

Question 1

A gaming company stores player session data as JSON documents. Each document contains fields like sessionId, userId, startTime, and a varying set of optional fields such as deviceType or campaignId. The application needs to query sessions by userId and startTime range using SQL-like queries, and also by sessionId with low latency. Which Azure Cosmos DB API should the company choose?

Accepted Answer

SQL (Core) API. The SQL (Core) API is the correct choice because it natively supports SQL-like queries over JSON documents, enabling efficient filtering by userId and startTime range. It also provides low-latency point reads by sessionId when a well-designed partition key (e.g., /userId) is used, and it offers automatic indexing of all JSON properties, including optional fields like deviceType or campaignId.

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MongoDB API

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Table API

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Gremlin (Graph) API

Question 2

A mobile game developer needs to store player session data. Each session has a unique SessionID, a UserID, a start timestamp, an end timestamp, and a collection of game events (each event is a JSON object). The application requires low-latency point reads by SessionID and the ability to query all sessions for a given UserID within a time range. The schema of game events can vary between sessions (e.g., new event types added frequently). The developer wants a fully managed NoSQL database that supports flexible schemas and secondary indexing. Which Azure data store should they choose?

Accepted Answer

Azure Cosmos DB with the NoSQL API. Azure Cosmos DB with the NoSQL API is the correct choice because it provides a fully managed, globally distributed NoSQL database with native support for flexible schemas (schemaless JSON documents), low-latency point reads by partition key (SessionID), and automatic secondary indexing for querying by UserID within a time range. Its ability to handle varying game event schemas without schema migrations makes it ideal for this use case.

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Azure Table Storage

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Azure Blob Storage

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Azure Cache for Redis

Question 3

A retail chain captures real-time sales data from point-of-sale (POS) systems as a stream of events. The data is ingested into Azure Event Hubs. Additionally, the company receives daily inventory files in CSV format uploaded to Azure Data Lake Storage Gen2. The analytics team needs to combine the streaming sales data with the batch inventory data to generate near real-time dashboards and run historical reports. They want a single analytics platform that can handle both streaming and batch workloads, and allow querying data directly in the data lake using SQL. Which Azure service should they choose?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that natively integrates with Azure Event Hubs for real-time streaming ingestion and Azure Data Lake Storage Gen2 for batch data. Its Synapse SQL engine supports querying data directly in the data lake using T-SQL, enabling near real-time dashboards and historical reports without data movement. This service is designed to handle both streaming and batch workloads in a single workspace, meeting all the stated requirements.

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Azure Stream Analytics

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Azure Data Lake Analytics

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Azure HDInsight

Question 4

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?

Accepted Answer

Azure Blob Storage for videos and Azure Cosmos DB for metadata. Azure Blob Storage is optimized for storing large binary objects like video files, offering high-throughput streaming via HTTP/HTTPS and integration with CDN for low-latency global delivery. Azure Cosmos DB provides single-digit millisecond read and write latencies with automatic indexing, making it ideal for quickly querying JSON metadata by tag using SQL or MongoDB API. This combination separates storage concerns (blobs for raw video, document DB for structured metadata) to meet both streaming and query performance requirements.

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Azure Blob Storage for both videos and metadata

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Azure Cosmos DB for videos and Azure Table Storage for metadata

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Azure Files for videos and Azure SQL Database for metadata

Question 5

A SaaS company hosts a multi-tenant application. Each tenant has a separate Azure SQL Database. The databases are small (1-3 GB) and their workloads vary significantly over time, with some tenants active during business hours and others at night. The company wants to maximize resource utilization and minimize costs by pooling compute resources across tenants while maintaining predictable performance per database. Which Azure SQL Database deployment option should they choose?

Accepted Answer

Azure SQL Database Elastic Pool. Azure SQL Database Elastic Pool is the correct choice because it allows multiple databases (tenants) to share a fixed pool of compute and storage resources, enabling cost efficiency through resource pooling while providing predictable performance via per-database resource limits (min/max DTU or vCore). This matches the scenario of small databases with variable, non-overlapping workloads across tenants.

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Azure SQL Database Single Database

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Azure SQL Managed Instance

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SQL Server on Azure Virtual Machine

Question 6

A retail company is designing a product catalog for its e-commerce website. Each product has a unique ProductID, a name, a price, and a variable number of attributes (e.g., size, color, weight) that differ across product categories. The application requires ability to read a product's details by ProductID with single-digit millisecond latency from any Azure region globally. The schema must be flexible to accommodate new attributes without schema changes. Which Azure data store should the company choose?

Accepted Answer

Azure Cosmos DB using the NoSQL API. Azure Cosmos DB with the NoSQL API is correct because it provides a fully managed, globally distributed NoSQL database that supports flexible schemas (allowing variable product attributes without schema changes) and guarantees single-digit millisecond read latency at any scale from any Azure region via its multi-region write and read replicas. The unique ProductID serves as a natural partition key, enabling efficient point reads with consistent low latency.

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Azure Table Storage

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Azure SQL Database

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Azure Blob Storage

Question 7

A mobile gaming company stores player session data as key-value pairs. Each player has a unique PlayerID, and the application needs to read/write the player's current level and score with very low latency. The data does not require complex queries, and the schema (attributes per player) can vary. The company wants a fully managed, globally distributed NoSQL database. Which Azure data store should they choose?

Accepted Answer

Azure Cosmos DB Table API. Azure Cosmos DB Table API is the correct choice because it provides a fully managed, globally distributed NoSQL database that supports key-value data with schema flexibility. It offers low-latency reads and writes (single-digit milliseconds at the 99th percentile) and automatic global distribution, making it ideal for storing player session data with varying attributes per player.

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Azure SQL Database

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Azure Blob Storage

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Azure Cache for Redis

Question 8

A smart city application collects sensor data from thousands of devices. Data is ingested as JSON messages containing deviceId, timestamp, and reading value. The application must support fast point reads by deviceId and also run queries to retrieve all readings for a specific deviceId within a time range. The development team prefers a SQL-like query language. Which Azure Cosmos DB API should they choose?

Accepted Answer

SQL (Core) API. The SQL (Core) API is the best choice because it natively supports SQL-like querying, enabling both fast point reads by deviceId (using the partition key) and efficient time-range queries on a specific deviceId. It also provides native JSON support, which aligns with the JSON message format from the sensors, and allows indexing on timestamp for range queries.

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Table API

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MongoDB API

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Cassandra API

Question 9

A smart home company stores sensor readings from thousands of devices in Azure Cosmos DB. Each reading includes a deviceID, timestamp (ISO format), sensor type, and value. The most common query retrieves all readings for a specific device within a time range. To minimize Request Units (RU) consumption and ensure even data distribution, which property should be chosen as the partition key?

Accepted Answer

A) deviceID. DeviceID is the correct partition key because it is the primary filter in the most common query (all readings for a specific device within a time range). Partitioning by deviceID ensures that all readings for a single device are stored in the same logical partition, making queries highly efficient by targeting a single partition. It also provides even data distribution across physical partitions, as thousands of devices will have roughly equal numbers of readings, minimizing RU consumption.

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B) timestamp

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C) sensor type

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D) value

Question 10

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?

Accepted Answer

Streaming processing. B is correct because streaming processing is designed for continuous, real-time data ingestion and immediate analysis, which matches the requirement to process sensor data as it arrives. Technologies like Azure Stream Analytics or Apache Kafka enable low-latency processing of IoT data streams to detect anomalies and predict failures without batching.

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Batch processing

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Online transaction processing (OLTP)

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Data warehousing