DP-900 Describe considerations for working with non-relational data on Azure Practice Test 10 — 15 Questions

Question 1

A mobile app stores user preferences in Azure Cosmos DB using the NoSQL API. The app frequently reads a single user's profile by user ID (the partition key). The development team wants the fastest possible read performance globally and is willing to accept that reads might not reflect the latest write immediately. Which consistency level should they choose to minimize read latency?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Eventual. Eventual consistency offers the lowest read latency because it allows reads to return data from any replica without waiting for confirmation that the write has been fully replicated. Since the app can tolerate stale reads (i.e., not reflecting the latest write immediately), Eventual consistency eliminates the synchronization overhead required by stronger models, making it the fastest choice for global read performance.

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Strong

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Bounded staleness

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Session

Question 2

A company needs to store archived log files that are rarely accessed but must be retained for regulatory compliance. The logs are text-based and each file is about 10 MB. They want the lowest storage cost while ensuring the data is durable and can be read when needed. Which Azure Blob Storage access tier should they choose?

Accepted Answer

Archive. The Archive tier is the correct choice because it offers the lowest storage cost for data that is rarely accessed and must be retained for long periods. Archived log files that are text-based and 10 MB each fit this profile perfectly, as the Archive tier is designed for data that can tolerate a retrieval latency of several hours (up to 15 hours for standard priority) while providing the same high durability (99.9999999999% or 11 nines) as other tiers. The data remains fully durable and can be read when needed by first rehydrating it to an online tier (Hot, Cool, or Cold) before access.

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Hot

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Cool

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Cold

Question 3

A global social media app uses Azure Cosmos DB (NoSQL API) to store user profile data. The app is read-heavy and must serve content with the lowest possible latency to users worldwide. The data is updated by users, and the business has determined that eventual consistency is acceptable because immediate consistency after a write is not critical for profile views. Which consistency level should they choose to minimize read latency?

Accepted Answer

Eventual. Eventual consistency is the correct choice because it offers the lowest read latency by allowing reads to return data immediately without waiting for replication to complete. Since the app is read-heavy, global, and can tolerate eventual consistency for profile views, this consistency level minimizes the time to serve content by not imposing any ordering or staleness guarantees on replicas.

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Strong

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Bounded staleness

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Session

Question 4

A company stores user session data for a web application. Each session has a unique SessionID, UserID, start time, end time, and a variable set of attributes (e.g., pages visited, clicks, device type). The workload requires low-latency reads by SessionID and occasional queries by UserID and time range. Schema flexibility is critical because the attributes evolve over time. The team wants a fully managed NoSQL database that supports secondary indexing. Which Azure data store should they choose?

Accepted Answer

Azure Cosmos DB (NoSQL API). Azure Cosmos DB (NoSQL API) is correct because it is a fully managed NoSQL database that offers low-latency reads by SessionID (using a partition key), supports secondary indexing for queries by UserID and time range, and provides schema flexibility for evolving session attributes. Its multi-model API and global distribution meet the workload requirements without manual indexing or schema management.

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Azure SQL Database

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Azure Table Storage

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Azure Blob Storage

Question 5

A mobile gaming company stores player activity logs as JSON documents. Each document has a unique ActivityID, a PlayerID, a timestamp, and a variable set of attributes depending on the game event (e.g., level started, item purchased). The application requires low-latency point reads by ActivityID and needs to query logs by PlayerID for a given time range. Schema flexibility is critical because new game events are added frequently. Which Azure Cosmos DB API should they choose?

Accepted Answer

NoSQL API (formerly SQL API). The NoSQL API (formerly SQL API) is the correct choice because it natively supports JSON documents with flexible schemas, enabling the variable attributes required for new game events. It provides low-latency point reads by ActivityID via direct partition key lookups and supports efficient queries by PlayerID within a time range using composite indexes or cross-partition queries with filtering. This API is optimized for schema-agnostic, document-based workloads and offers the richest query capabilities for JSON data in Azure Cosmos DB.

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MongoDB API

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Cassandra API

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Gremlin API

Question 6

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?

Accepted Answer

Create a composite index on (deviceId ASC, timestamp DESC). 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.

Answer

Use the default indexing policy that automatically indexes all properties

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Disable indexing for all properties to speed up writes

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Create a spatial index on the deviceId field

Question 7

A gaming company stores player profiles in Azure Cosmos DB using the NoSQL API. Each profile is a JSON document containing fields like playerId, userName, level, inventory (an array of items), and friends (an array of playerIds). The application frequently needs to query all players that have a specific item in their inventory (e.g., 'sword'). Which Cosmos DB feature should they use to support this query efficiently?

Accepted Answer

Indexing policy with a wildcard index. A wildcard index in the indexing policy allows Azure Cosmos DB to automatically index all properties within a JSON document, including nested array elements like those in the 'inventory' array. This enables efficient queries such as 'SELECT * FROM c WHERE ARRAY_CONTAINS(c.inventory, {name: "sword"})' without requiring a custom composite index for each possible item. Without a wildcard index, the query would require a full scan of all documents, which is inefficient at scale.

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Change feed

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Stored procedures

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Composite index

Question 8

A company stores IoT sensor data as JSON files in Azure Blob Storage. A data analyst needs to run ad-hoc SQL queries on these files without moving the data and without provisioning any compute clusters. The analyst wants to pay only for the amount of data processed by each query. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool allows you to query data directly from Azure Blob Storage using T-SQL without provisioning any compute clusters. It uses a pay-per-query model where you are billed only for the amount of data processed, making it ideal for ad-hoc SQL queries on JSON files stored in Blob Storage without data movement.

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Azure SQL Database

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Azure Cosmos DB

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Azure Data Factory

Question 9

A mobile gaming startup needs to store player profiles that can have varying attributes (e.g., some players have a 'nickname', others have 'avatar URL'). The application must read a player's profile by PlayerID with very low latency (under 10 ms) from any location worldwide. The data does not require complex queries or joins. Which Azure data store should they choose?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it is a globally distributed, multi-model database service that guarantees single-digit-millisecond read latencies (under 10 ms) at any scale from any Azure region. Its schema-agnostic nature allows storing player profiles with varying attributes (e.g., nickname, avatar URL) without requiring a fixed schema, and it supports point reads by PlayerID with a consistency model that can be tuned for performance. This directly matches the requirements of low-latency global reads and flexible, non-relational data.

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Azure SQL Database

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Azure Table Storage

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Azure Blob Storage

Question 10

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?

Accepted Answer

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.

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The query is not using an index on the temperature field.

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The time range filter is too large, causing a full table scan.

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The document size is too large, increasing RU per read.

Question 11

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 12

A smart home company stores device telemetry in Azure Cosmos DB using the NoSQL API. Each document contains: deviceId (string), timestamp (datetime), temperature (float), humidity (float). The most common query retrieves all documents for a specific deviceId within a time range, ordered by timestamp descending. This query performs well. However, a new query that finds devices with temperature > 50 in the last hour (without specifying deviceId) is extremely slow and consumes many request units (RUs). What is the most likely cause?

Accepted Answer

The query does not specify the partition key (deviceId), causing a cross-partition query that scans every physical partition.. In Azure Cosmos DB NoSQL API, the partition key (deviceId) determines data distribution across physical partitions. Queries that do not include the partition key in the filter become cross-partition queries, which must fan out to every physical partition, scanning all documents. This is extremely slow and consumes many RUs, especially in large containers. The original query specifying deviceId performs well because it targets a single partition.

Answer

The temperature field is not indexed by default, so the query forces a full scan of all documents.

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The time range filter on timestamp cannot be combined with the temperature filter efficiently.

Answer

The default indexing policy only indexes strings and numbers as range indexes, but temperature is stored as a number and is indexed.

Question 13

A social media analytics company needs to store large amounts of user activity logs. Each log entry contains a timestamp, user ID, activity type, and a dynamic set of custom attributes (e.g., page viewed, time spent). The application requires low-latency writes and point reads by a composite key (user ID and timestamp). The data is rarely updated after insertion. The company wants a fully managed NoSQL database that supports serverless throughput and automatic expiration of old logs (TTL). Which Azure Cosmos DB API should they choose?

Accepted Answer

Table API. The Table API is the correct choice because it provides a fully managed, serverless NoSQL database with automatic TTL (Time-to-Live) for data expiration, low-latency point reads and writes by a composite key (partition key + row key), and is optimized for storing large volumes of structured log data with dynamic attributes. It supports the exact requirements: high-throughput writes, point queries by user ID and timestamp, and automatic expiration of old logs without manual intervention.

Answer

NoSQL API (Core/SQL API)

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Cassandra API

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Gremlin API

Question 14

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 15

Drag and drop the steps to configure a geo-replication for Azure Cosmos DB in the correct order.

Accepted Answer

Step 1: Navigate to the Azure Cosmos DB account. Step 2: Under Settings, click 'Replicate data globally'. Step 3: Select the regions you want to add. Step 4: Enable multi-region writes (if desired). Step 5: Click Save.. Geo-replication is configured by selecting regions and enabling multi-region writes if needed, then saving to initiate replication.

Answer

Step 1: Navigate to the Azure Cosmos DB account. Step 2: Under Settings, click 'Replicate data globally'. Step 3: Enable multi-region writes (if desired). Step 4: Select the regions you want to add. Step 5: Click Save.

Answer

Step 1: Navigate to the Azure Cosmos DB account. Step 2: Enable multi-region writes (if desired). Step 3: Under Settings, click 'Replicate data globally'. Step 4: Select the regions you want to add. Step 5: Click Save.

Answer

Step 1: Navigate to the Azure Cosmos DB account. Step 2: Under Settings, click 'Replicate data globally'. Step 3: Select the regions you want to add. Step 4: Click Save. Step 5: Enable multi-region writes (if desired).