DP-900 Describe considerations for working with non-relational data on Azure Practice Questions

Question 1

A global gaming company develops a multiplayer game. Player profile data (username, email, preferences) is stored as simple key-value pairs and must be accessible with single-digit millisecond latency from any region. Game session logs are stored as JSON documents with varying fields (session ID, player actions, timestamps) and must be queryable by player ID and timestamp range using SQL-like syntax. The company wants to use a single Azure database service for both workloads. Which combination of Azure Cosmos DB APIs should they choose?

Accepted Answer

Table API for profiles and SQL API for logs. The Table API provides a simple key-value store ideal for low-latency profile lookups, while the SQL API supports querying JSON documents with SQL-like syntax, enabling efficient queries on game session logs by player ID and timestamp range. This combination meets both workloads using Azure Cosmos DB, but because each Cosmos DB account supports only one API, the profiles and logs would need to be stored in separate Cosmos DB accounts (one with Table API and one with SQL API).

Answer

SQL API for both profiles and logs

Answer

MongoDB API for profiles and Cassandra API for logs

Answer

Table API for both profiles and logs

Question 2

A social networking application stores user profiles as JSON documents. Each profile can have different fields (e.g., education, work history, interests) depending on what the user fills in. The application also needs to traverse friend connections as a graph to recommend new friends. The development team wants to use a single Azure Cosmos DB account for both workloads. Which combination of Azure Cosmos DB APIs should they choose?

Accepted Answer

Gremlin API for both profiles and graph. A single Azure Cosmos DB account is provisioned with exactly one API. The Gremlin API stores data as vertices and edges with arbitrary properties, allowing it to handle flexible JSON-like profiles while also supporting Gremlin traversal queries for friend recommendations. The SQL API cannot perform Gremlin graph traversals, and the MongoDB/Table APIs lack graph support. Thus, option C is correct.

Answer

SQL API for profiles and Gremlin API for graph

Answer

MongoDB API for profiles and SQL API for graph

Answer

Table API for profiles and Gremlin API for graph

Question 3

A social media platform stores user profiles as JSON documents where each profile can have different attributes (e.g., education, work history, interests). The platform also needs to traverse friend connections to recommend new connections using graph queries. The development team wants to use a single Azure Cosmos DB account for both workloads while minimizing complexity. Which combination of Azure Cosmos DB APIs should they choose?

Accepted Answer

Gremlin API only. The Gremlin API supports both graph traversal (for friend connections) and document storage (user profiles as JSON documents in vertex properties) within a single API. Using only the Gremlin API in one Azure Cosmos DB account minimizes complexity, as a single account cannot host multiple APIs. Options A, B, and C are incorrect because they require combining two different APIs in one account, which is not supported—each Azure Cosmos DB account is tied to a single API.

Answer

SQL API and Gremlin API

Answer

MongoDB API and Table API

Answer

Cassandra API and SQL API

Question 4

A social media application stores user posts as JSON documents. Each post contains fields like post_id, author, content, and timestamp. The application needs to query posts by author and date range using SQL-like queries. Additionally, the application requires the ability to traverse follower relationships as a graph to suggest new friends. The development team wants to use a single Azure Cosmos DB account to minimize management overhead. Which combination of Azure Cosmos DB APIs should they choose?

Accepted Answer

Use the SQL API for the posts and the Gremlin API for the follower graph.. Azure Cosmos DB accounts are locked to a single API at creation. It is not possible to have both SQL and Gremlin APIs within the same account. To satisfy both document query and graph traversal requirements, separate Cosmos DB accounts must be used—one with the SQL API for posts and another with the Gremlin API for the follower graph. None of the provided options achieve this with a single account.

Answer

Use the SQL API only for both workloads.

Answer

Use the MongoDB API only for both workloads.

Answer

Use the Gremlin API only for both workloads.

Question 5

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?

Accepted Answer

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.

Answer

A. Table API

Answer

B. Cassandra API

Answer

C. MongoDB API

Question 6

A logistics company stores shipment tracking data as JSON documents in Azure Cosmos DB. Each document contains fields like trackingId, origin, destination, status, weight, and optional fields (estimatedDelivery, carrierNotes). The application needs to perform low-latency lookups by trackingId and also run queries to find all shipments that have a specific origin and status. Which Azure Cosmos DB API should they choose?

Accepted Answer

SQL (Core) API. The SQL (Core) API is the best choice because it provides native support for querying JSON documents with a SQL-like syntax, enabling efficient low-latency lookups by trackingId (using a partition key) and flexible queries on fields like origin and status. It also supports indexing on any JSON property, making queries on optional fields like estimatedDelivery or carrierNotes performant without schema management.

Answer

Table API

Answer

MongoDB API

Answer

Gremlin API

Question 7

A global e-commerce company uses Azure Cosmos DB with multiple write regions to handle high traffic from users worldwide. For their order processing system, they must guarantee that once an order is recorded, all subsequent reads from any region see the most up-to-date order status. However, they also need low write latency globally. Which configuration should they choose to meet these requirements?

Accepted Answer

Use single-region writes with strong consistency. Strong consistency with single-region writes ensures that all reads in any region return the most recent write, because Cosmos DB replicates writes synchronously to all regions when strong consistency is configured. This guarantees linearizability: once an order is committed, every subsequent read sees that update. Single-region writes avoid the conflict-resolution overhead of multi-region writes while still providing low write latency within the primary region, and reads from secondary regions are served from locally replicated data that is kept fully consistent.

Answer

Use multi-region writes with strong consistency

Answer

Use multi-region writes with bounded staleness consistency

Answer

Use single-region writes with eventual consistency and implement application-level conflict resolution

Question 8

A data engineering team is designing an ELT (Extract, Load, Transform) pipeline using Azure Data Lake Storage Gen2. They will ingest raw sales data from multiple sources, store it in the data lake, transform it using Azure Databricks, and finally store the transformed data in a curated zone for analytics. Place the steps in the correct order for an ELT pipeline.

Accepted Answer

Ingest raw data from sources. In an ELT pipeline, data is first extracted (ingested) from sources, then loaded into a storage system (Azure Data Lake Storage Gen2). After loading, the data is transformed using a processing engine like Azure Databricks, and finally the transformed data is written to a curated zone for analytics. This order follows the ELT paradigm: Extract, Load, Transform.

Question 9

A global gaming company uses Azure Cosmos DB to store player scores and profiles. The application reads and writes player data from multiple regions worldwide. The company wants to ensure that when a player updates their high score in one region, any subsequent read from another region will always see the latest value, even if there is network latency between regions. Which consistency level should they choose?

Accepted Answer

Eventual. When multi-region writes are enabled in Azure Cosmos DB, the only supported consistency level is eventual consistency. However, eventual consistency does not guarantee that reads always return the latest value—replicas may lag. Therefore, none of the given consistency levels can satisfy the requirement of always seeing the latest value across regions after a write in one region. The company would need to reconsider their architecture, such as using a single write region with strong consistency. Thus, no option is correct.

Answer

Strong

Answer

Bounded staleness

Answer

Session

Question 10

A company archives legal documents that must be kept for 10 years. Access to these documents is extremely rare (maybe once a year). They want to minimize storage costs. Which Azure Blob Storage access tier is most cost-effective for this data?

Accepted Answer

Archive tier. The Archive tier is the most cost-effective for data that is accessed less than once a year and must be retained for a long period (10 years). It offers the lowest storage cost per GB among all Azure Blob Storage access tiers, but has the highest access and data retrieval costs, making it ideal for rarely accessed, long-term archival data like legal documents.

Answer

Hot tier

Answer

Cool tier

Answer

Cold tier

Describe considerations for working with non-relational data on Azure

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