DP-900 Practice Test 10 — 10 Questions

Question 1

A global social media startup stores user profiles as JSON documents in Azure Cosmos DB. Their application frequently reads profiles by user ID and also runs queries to find users based on location or interests. The workload is read-heavy with high throughput requirements. The operations team notices that query performance degrades during peak hours. Which action would most effectively improve query performance?

Accepted Answer

Increase the provisioned throughput (RU/s). Increasing the provisioned throughput (RU/s) directly allocates more processing capacity to the Cosmos DB container, allowing it to handle higher request volumes and reduce throttling during peak hours. Since the workload is read-heavy and query performance degrades under high throughput demands, raising RU/s is the most effective and immediate action to improve performance.

Answer

Increase the number of containers

Answer

Choose a different API (e.g., switch from SQL API to MongoDB API)

Answer

Switch to a different Azure region

Question 2

A social media company stores user posts in Azure Cosmos DB. Posts are frequently queried by user ID and creation timestamp. To minimize Request Units (RU) per query, which property should be chosen as the partition key?

Accepted Answer

User ID. User ID is the correct partition key because it evenly distributes writes and reads across physical partitions, ensuring that queries filtering by user ID and timestamp are scoped to a single partition. This minimizes cross-partition queries, which consume more Request Units (RU) than single-partition queries. Azure Cosmos DB routes each query to the partition containing the matching partition key value, so choosing User ID keeps most queries efficient.

Answer

Timestamp

Answer

Post content

Answer

A composite key of user ID and timestamp

Question 3

A social media company stores user posts in Azure Cosmos DB. Each post document contains fields like postId, userId, content, timestamp, and an array of comments. The comments array can grow large (hundreds per post), and the application frequently retrieves a post without its comments to display in a feed. To optimize read performance and minimize request units (RU) consumption, which data modeling approach should the company adopt?

Accepted Answer

B. Store comments as separate documents and reference them from the post document via a comments array of IDs.. Option B is correct because storing comments as separate documents and referencing them via an array of IDs in the post document allows the application to retrieve the post without comments in a single point read, consuming minimal request units (RUs). This avoids loading the large comments array when only the post metadata is needed for the feed, significantly reducing RU consumption and improving read performance in Azure Cosmos DB.

Answer

A. Store comments in a separate container to isolate the data.

Answer

C. Use a vertical partition within the same document to separate the comments array.

Answer

D. Migrate the data to Azure SQL Database to use normalized tables and indexes.

Question 4

A social media application stores user posts in Azure Cosmos DB. Each document contains fields: PostID (unique), UserID, Timestamp, Content, LikesCount. The most common query retrieves all posts by a specific UserID ordered by Timestamp descending. Which partition key and indexing strategy minimizes Request Unit (RU) consumption?

Accepted Answer

Partition key: UserID; Index: range on Timestamp. Option B is correct because the query filters on UserID, so setting UserID as the partition key ensures all posts for a user are in the same physical partition, avoiding cross-partition queries. Adding a range index on Timestamp allows efficient sorting without additional RU overhead, as Cosmos DB can use the index to return results in descending order directly.

Answer

Partition key: PostID; Index: range on Timestamp

Answer

Partition key: Timestamp; Index: range on UserID

Answer

Partition key: UserID; Index: composite on (UserID, PostID)

Question 5

An e-commerce application uses Azure SQL Database. The Orders table stores millions of rows with columns: OrderID (primary key, clustered index), CustomerID, OrderDate, OrderStatus, TotalAmount. Queries frequently filter on OrderDate and OrderStatus, and sort results by OrderDate DESC. Which indexing strategy will most improve query performance for these filters and sort?

Accepted Answer

Create a nonclustered index on OrderDate, OrderStatus and include other columns needed by the query.. Option B is correct because a nonclustered index on (OrderDate, OrderStatus) supports both the filter and the sort in a single index seek/scan. SQL Server can use the index to locate rows matching both predicates and return them already sorted by OrderDate DESC without a separate sort operation, which is critical for performance on millions of rows.

Answer

Create a nonclustered index on OrderDate only.

Answer

Create a clustered columnstore index on the table.

Answer

Create a nonclustered index on OrderStatus only.

Question 6

A startup has an application with unpredictable usage patterns on Azure SQL Database. They want to minimize cost by paying only for the compute they use and the database should automatically pause during idle periods. Which Azure SQL Database option should they choose?

Accepted Answer

Serverless. Azure SQL Database Serverless is designed for applications with unpredictable usage patterns, as it automatically scales compute resources based on demand and pauses the database during idle periods to eliminate compute costs. This model charges per second for the compute used, making it the most cost-effective choice for workloads that have periods of inactivity.

Answer

Provisioned (DTU or vCore)

Answer

Hyperscale

Answer

Business Critical

Question 7

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?

Accepted Answer

Azure SQL Database elastic pool. Azure SQL Database elastic pool is the most suitable choice because it allows multiple single databases (one per tenant) to share a fixed set of resources (DTUs or vCores) within a pool, optimizing cost by averaging resource usage across tenants. This model supports scaling to thousands of tenants while providing resource elasticity and predictable pricing, as tenants with low activity can use unused capacity from busy ones without over-provisioning.

Answer

Azure SQL Database (single database)

Answer

Azure SQL Managed Instance

Answer

SQL Server on Azure Virtual Machine

Question 8

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?

Accepted Answer

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.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Table Storage

Question 9

An e-commerce application uses Azure SQL Database. During flash sales, the database experiences high CPU usage and query timeouts. The team needs a solution that can handle sudden increases in demand without downtime. Which scaling option should they choose?

Accepted Answer

Hyperscale service tier. The Hyperscale service tier is designed for high-performance, rapidly growing workloads that require instant scalability. It separates compute from storage, allowing compute nodes to be added or scaled up in seconds without downtime, making it ideal for handling sudden spikes in demand like flash sales.

Answer

Read scale-out

Answer

Elastic Pool

Answer

Geo-replication

Question 10

A social networking application needs to store and query relationships between users, such as 'friends of friends'. The application should be able to traverse these relationships efficiently to recommend new connections. Which Azure NoSQL data store and API should they choose?

Accepted Answer

Azure Cosmos DB with the Gremlin API. Azure Cosmos DB with the Gremlin API is the correct choice because Gremlin is a graph traversal language specifically designed for querying highly connected data, such as social network relationships. It allows efficient traversal of edges (e.g., 'friends of friends') using graph algorithms, which is exactly what the application needs for recommending new connections. Other APIs like Table API or services like Blob Storage lack native graph traversal capabilities.

Answer

Azure Cosmos DB with the Table API

Answer

Azure Cache for Redis

Answer

Azure Blob Storage