Describe Considerations for Working with Non-Relational Data on Azure

Understand Azure storage capabilities for blobs, files, and tables, and describe Azure Cosmos DB features, use cases, and APIs.

5 minutes 5 Questions

When working with non-relational data on Azure, there are several important considerations to keep in mind. Non-relational data, also known as NoSQL data, differs from traditional relational databases in that it does not rely on structured tables with fixed schemas. Azure offers multiple services t…

Concepts covered
Azure Blob StorageBlob Storage Access Tiers and LifecycleAzure File StorageAzure Data Lake Storage Gen2Azure Cosmos DB Overview and Use CasesAzure Cosmos DB API for MongoDBAzure Cosmos DB APIs for Cassandra, Table, and GremlinAzure Table StorageChoosing Azure Storage ServicesAzure Cosmos DB API for NoSQLAzure Cosmos DB API for PostgreSQLCosmos DB Request Units and PartitioningCosmos DB Consistency Levels
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DP-900 - Describe Considerations for Working with Non-Relational Data on Azure Example Questions

Test your knowledge of Describe Considerations for Working with Non-Relational Data on Azure

Question 1

A team wants to store holiday photos that users upload through a mobile app. The photos can be any size and format. Which Azure service should they use?

Correct Answer: Azure Blob Storage, since it handles unstructured data like images

Azure Blob Storage is specifically designed to store large amounts of unstructured data such as images, videos, and documents, making it the ideal choice for storing holiday photos of any size and format.

Azure Table Storage is a NoSQL key-value store for structured data, not for storing binary files like images. Azure Queue Storage is a messaging service used to pass messages between application components, not for persisting file data. Azure File Storage provides SMB-based file shares typically used for lift-and-shift scenarios and shared access across VMs, not optimized for app-driven media uploads.

Key concept tested: understanding Azure storage types and identifying Blob Storage as the solution for unstructured binary data.

Question 2

Which use case is Azure Cosmos DB best suited for among the following options?

Correct Answer: Real-time applications requiring low latency at global scale

Azure Cosmos DB is purpose-built for real-time applications requiring low latency (single-digit millisecond response times) with turnkey global distribution, making it ideal for globally scaled, highly responsive applications.

Batch processing of large on-premises datasets is better handled by tools like Apache Spark or Azure Data Factory, not a globally distributed NoSQL database. Complex relational joins across normalized tables are the domain of relational databases like Azure SQL Database, as Cosmos DB is a NoSQL service that favors denormalized data models. Static reporting dashboards with infrequent refreshes do not justify the cost or capabilities of Cosmos DB and are better served by simpler storage and reporting tools.

Key concept tested: understanding Azure Cosmos DB's core strengths as a globally distributed, low-latency NoSQL database.

Question 3

What types of data is Azure Data Lake Storage Gen2 designed to store and process?

Correct Answer: Large volumes of structured, semi-structured, and unstructured data for analytics workloads

Azure Data Lake Storage Gen2 is specifically designed to handle large volumes of structured, semi-structured, and unstructured data, making it ideal for big data analytics workloads by combining the scalability and cost benefits of Azure Blob Storage with the hierarchical file system capabilities of Azure Data Lake Storage Gen1.

It is not limited to structured relational data, as it explicitly supports unstructured data like images, videos, and log files. It is not restricted to semi-structured documents like JSON and XML, nor is it designed for real-time application serving. It is built for massive-scale data, not small volumes suited for lightweight reporting.

Key concept tested: understanding the purpose and data type versatility of Azure Data Lake Storage Gen2 for analytics.

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