DP-900 Describe considerations for working with non-relational data on Azure Practice Test 5 — 15 Questions

Question 1

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.

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Hot tier

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Cool tier

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Cold tier

Question 2

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.

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

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

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

Question 3

A media company stores video metadata in Azure Table Storage. Each video has a unique VideoID, and the application frequently queries for videos uploaded on a specific date. The current table uses PartitionKey = VideoID and RowKey = UploadDate. Queries filtering by UploadDate are slow and consume many transactions. Which design change will most optimize queries that retrieve all videos from a given date?

Accepted Answer

C. Change the PartitionKey to a date-based value (e.g., YYYY-MM-DD) and use VideoID as the RowKey.. Option C is correct because Azure Table Storage queries are most efficient when the PartitionKey is used as the primary filter. By changing the PartitionKey to a date-based value (e.g., YYYY-MM-DD), queries for all videos uploaded on a specific date become partition scans, which are fast and consume minimal transactions. Using VideoID as the RowKey still allows unique identification of each video within that date partition.

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A. Use UploadDate as the RowKey only, but keep PartitionKey as VideoID.

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B. Create a secondary index on UploadDate.

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D. Migrate the data to Azure Cosmos DB Table API for better indexing.

Question 4

A company stores user profile images in Azure Blob Storage. Each image is accessed via a URL that includes a Shared Access Signature (SAS) token generated using the storage account key. The company needs to immediately revoke access to all images for a specific user. Which action should they take?

Accepted Answer

Change the storage account access keys.. Option B is correct because changing the storage account access keys invalidates all SAS tokens that were generated using those keys, including any existing tokens. This immediately revokes access to all images for all users, including the specific user, without needing to manage individual tokens. SAS tokens are signed with the account key, so rotating the key renders all tokens generated with the old key invalid.

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Delete the individual SAS tokens associated with that user's images.

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Delete the container containing the user's images.

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Regenerate the SAS token for each image.

Question 5

A development team is designing an application that stores user session data in Azure Cosmos DB. Each session document contains a sessionId (unique), userId, timestamp, and a JSON field 'metadata' that can include various optional properties. The application frequently queries by userId to retrieve all sessions for a particular user. Which property should be chosen as the partition key to optimize query performance and ensure even data distribution?

Accepted Answer

userId. The partition key should be the property most frequently used in queries and that provides high cardinality for even distribution. Since the application frequently queries by userId to retrieve all sessions for a user, choosing userId as the partition key ensures that all session documents for a given user are stored in the same logical partition, making these queries efficient and fast. Additionally, userId typically has a large number of distinct values, which promotes even data distribution across physical partitions.

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sessionId

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timestamp

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metadata

Question 6

A startup is building a global user session store. Each session consists of a simple key (session ID) and a value (user data as a JSON string). The application requires low-latency reads and writes from any Azure region, and the data must be durable. Which Azure service is best suited for this scenario?

Accepted Answer

Azure Cosmos DB (Table API). Azure Cosmos DB (Table API) is the best fit because it provides global, multi-region writes with tunable consistency, guaranteed single-digit-millisecond latency for reads and writes at the 99th percentile, and full durability with automatic replication across any number of Azure regions. The Table API offers a key-value store interface (session ID as partition key, JSON value) while also supporting schema flexibility and SLA-backed performance, which is critical for a global user session store.

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Azure Table Storage

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Azure Redis Cache

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Azure Blob Storage

Question 7

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.

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Timestamp

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Post content

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A composite key of user ID and timestamp

Question 8

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 9

A media company needs to store thousands of high-resolution videos. Each video is up to 10 GB in size and must be accessible via HTTP/HTTPS URLs for playback. The company does not require a file system hierarchy or SMB protocol support. Which Azure storage solution is most appropriate for this scenario?

Accepted Answer

Azure Blob Storage. Azure Blob Storage is designed for storing massive amounts of unstructured data, such as high-resolution videos, and provides HTTP/HTTPS access via URLs. It supports objects up to 4.77 TiB (or larger with premium block blobs), easily accommodating 10 GB files, and offers no file system hierarchy or SMB protocol, matching the company's requirements exactly.

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Azure Files

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Azure Queue Storage

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Azure Table Storage

Question 10

A mobile game stores player achievements in Azure Cosmos DB. Each player has a PlayerID, and achievements are stored as JSON documents with varying fields. The most common query retrieves all achievements for a specific player. To ensure low latency and efficient throughput, which property should be chosen as the partition key?

Accepted Answer

PlayerID. PlayerID is the correct partition key because the most common query retrieves all achievements for a specific player, and partitioning on PlayerID ensures that all documents for a given player are stored in the same physical partition. This allows the query to target a single partition, minimizing cross-partition queries and providing low latency and efficient throughput.

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Timestamp

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AchievementType

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Region

Question 11

A logistics company stores sensor data from delivery trucks in Azure Table Storage. Each sensor reading includes a TruckID, Timestamp, Location, and EngineTemperature. The most common query retrieves all readings for all trucks within a specific one-hour time window (e.g., between 10:00 and 11:00 on a given day). Currently, the table uses PartitionKey = TruckID and RowKey = Timestamp (ISO format). However, queries filtering by time range are slow and consume many transactions. Which design change will most improve the performance of these time-range queries?

Accepted Answer

Change PartitionKey to a date-based value (e.g., YYYY-MM-DD) and RowKey to a composite of TruckID and Timestamp.. Option A is correct because Azure Table Storage queries are most efficient when they target a specific PartitionKey and a range of RowKey values. By setting PartitionKey to a date-based value (e.g., YYYY-MM-DD), all readings for a given day are co-located in the same partition. Then, using a composite RowKey of TruckID and Timestamp allows the query to filter by time range within that partition using a single partition scan, drastically reducing the number of transactions and improving performance.

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Change RowKey to be a composite of TruckID and Timestamp while keeping PartitionKey as TruckID.

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Use Azure Cosmos DB with a partition key on Timestamp instead of Azure Table Storage.

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Enable indexing on the Timestamp column in Azure Table Storage.

Question 12

A social media startup stores user profile data, posts, and comments in Azure Cosmos DB. They notice that the logical partition size for a popular user's profile is growing beyond 20 GB, causing performance issues. The current partition key is 'userId'. Which action should they take to solve this?

Accepted Answer

Change the partition key to a synthetic key combining userId and postId. A is correct because the logical partition size limit in Azure Cosmos DB is 20 GB. By using a synthetic partition key that combines 'userId' and 'postId', you distribute the data for the popular user across multiple logical partitions, preventing any single partition from exceeding the 20 GB limit and resolving the performance bottleneck.

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Increase the RU/s

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Split the container into multiple containers by userId range

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Use a different API like MongoDB

Question 13

A ride-sharing application uses Azure Cosmos DB for trip data. Each trip record contains TripID (unique), DriverID, RiderID, TripDate, and other details. The most common query retrieves all trips for a specific driver within a given date range. Which partition key should be chosen to minimize Request Unit (RU) consumption and ensure even data distribution?

Accepted Answer

DriverID. DriverID is the optimal partition key because the most common query filters on DriverID and a date range. Partitioning by DriverID ensures that all trips for a specific driver are stored in the same physical partition, making the query a single-partition operation that consumes minimal Request Units (RUs). It also provides even data distribution across partitions because each driver generates a roughly similar number of trips, avoiding hot spots.

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TripID

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TripDate

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RiderID

Question 14

A global e-commerce platform uses Azure Cosmos DB to store product inventory data. Customers add items to their cart, which reduces the available inventory count. The application requires that after a customer adds an item, any subsequent read of that product's inventory from any region in the world must reflect the reduced count immediately. Which Cosmos DB consistency level should be used?

Accepted Answer

Strong consistency. Strong consistency ensures that any read operation returns the most recent write, regardless of the region. Since the application requires that after a customer adds an item, any subsequent read of that product's inventory from any region must reflect the reduced count immediately, Strong consistency is the only level that guarantees linearizability and zero staleness across all replicas.

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Eventual consistency

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Consistent prefix consistency

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Session consistency

Question 15

A hospital stores patient vital signs data in Azure Cosmos DB. Each document contains PatientID, Timestamp, HeartRate, BloodPressure, and other measurements. The most common query retrieves all vital signs for a specific patient within a time range (e.g., last 24 hours). Which property should be chosen as the partition key to minimize Request Unit (RU) consumption and ensure even data distribution?

Accepted Answer

PatientID. PatientID is the ideal partition key because the most common query filters by PatientID and a time range. With PatientID as the partition key, Cosmos DB can route the query to a single physical partition containing all documents for that patient, minimizing cross-partition queries and reducing RU consumption. It also ensures even data distribution since each patient generates a similar volume of vital signs data, avoiding hot partitions.

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Timestamp

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HeartRate

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BloodPressure