Microsoft Azure Data Fundamentals DP-900 Practice Question
Your company is designing a large-scale analytics architecture in Azure. The team needs a storage layer that can ingest and keep raw structured, semi-structured, and unstructured data without applying a schema up front, while scaling to petabytes at a comparatively low cost. Which type of analytical data store should they choose?
A NoSQL key-value store implemented with Azure Cosmos DB Table API
An in-memory cache hosted on Azure Cache for Redis
A relational data warehouse using Azure Synapse dedicated SQL pool
A data lake built on Azure Data Lake Storage Gen2 is designed for schema-on-read scenarios, allowing any data type to be stored in its raw form. It offers petabyte-scale capacity and is priced for bulk storage, making it suitable for large-scale analytics. A dedicated SQL pool in Azure Synapse is a relational data warehouse that enforces schema-on-write and is optimized for structured data only. Azure Cosmos DB's Table API is a NoSQL key-value store tuned for operational workloads rather than analytical, large-volume data processing. Azure Cache for Redis is an in-memory cache intended for low-latency data retrieval, not for persistent, low-cost, petabyte-scale storage.
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Microsoft Azure Data Fundamentals DP-900
Describe an analytics workload
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