Hotspot Question You are designing a storage solution that will ingest, store, and analyze petabytes (PBs) of structured, semi-structured, and unstructured text data. The analyzed data will be…

Question

Exhibit

Answer Area

• For storage and interactive analytics:
  Azure Data ExplorerAzure Data Lake AnalyticsLog Analytics
• Query language:
  KQLTransact-SQLU-SQL

Explanation

This hotspot question tests your knowledge of Azure services for large-scale data ingestion, storage, and interactive analytics, specifically identifying the right service for storing processed data and the right service for providing interactive analytics with flexible scaling options.

Approach. For storing processed petabytes of structured, semi-structured, and unstructured text data, Azure Synapse Analytics (specifically its dedicated SQL pools or Spark pools) is the correct recommendation, as it is purpose-built for large-scale data warehousing and analytics workloads that integrate with Azure Data Lake Storage Gen2. For providing interactive analytics with support for manual scaling, built-in autoscaling, and custom autoscaling, Azure Synapse Analytics Spark pools fulfill all three scaling requirements - manual scaling lets you set node counts, autoscale automatically adjusts cluster size based on workload, and custom autoscaling lets you define min/max node thresholds. Alternatively, the 'stores the processed data' drop-down maps to Azure Synapse Analytics and the 'provides interactive analytics' drop-down maps to Azure Synapse Analytics as well, since it supports interactive querying via SQL on-demand (serverless) and Spark. The key differentiator is that Azure Databricks also supports autoscaling but the question's combination of requirements (interactive analytics + three scaling modes + integration with ADLS Gen2 for long-term offload) aligns most directly with Azure Synapse Analytics.

Concept tested. Selecting the appropriate Azure storage and analytics services (Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Databricks) for petabyte-scale data workloads, and understanding which services support interactive analytics and the three scaling models: manual, built-in autoscaling, and custom autoscaling.

Reference. https://learn.microsoft.com/en-us/azure/synapse-analytics/overview-what-is

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