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Describe an analytics workload on AzuremediumMultiple ChoiceObjective-mapped

DP-900 Describe an analytics workload on Azure Practice Question

A retail company wants to analyze years of historical sales data stored as CSV files in Azure Blob Storage. The analytics solution must be serverless, allow T-SQL queries without managing infrastructure, and integrate directly with Power BI. Which Azure service should the company use?

⚠ Common exam trap

Candidates often confuse Azure SQL Database (a provisioned database) with a serverless query service, or they mistakenly think Azure Analysis Services can directly query raw files, when in fact it requires pre-loaded data models.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Azure Synapse Analytics serverless SQL pool

Azure Synapse Analytics serverless SQL pool is the correct choice because it provides a serverless, on-demand query service that can directly query CSV files stored in Azure Blob Storage using T-SQL without requiring any infrastructure management. It integrates natively with Power BI via the T-SQL endpoint, enabling direct data visualization from the queried files.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Azure SQL Database

    Why it's wrong here

    Azure SQL Database is a fully managed relational database service that runs with provisioned, always-on compute, making it suitable for OLTP but not for bursty, ad-hoc analytical queries over files. To analyze historical sales data in Blob Storage, data would first have to be imported into tables, and you would still pay for idle compute time. Its architecture does not include the serverless, schema-on-read query capability needed to directly scan CSV or Parquet files with T-SQL.

  • Azure Synapse Analytics serverless SQL pool

    Why this is correct

    Azure Synapse Analytics serverless SQL pool is a distributed query engine that reads data directly from Azure Blob Storage or Azure Data Lake Storage Gen2 using ordinary T-SQL and OPENROWSET. It has no infrastructure to manage; compute is automatically allocated per query and you pay only for the amount of data scanned, not for provisioned servers. This makes it ideal for analyzing years of historical sales files and it can integrate directly with Power BI for reports without moving data first.

  • Azure Cosmos DB

    Why it's wrong here

    Azure Cosmos DB is a multi-model NoSQL database engineered for low-latency, globally distributed operational workloads, not for analytical querying of external files. Its SQL API supports a restricted set of SQL over JSON documents stored within Cosmos DB, but it does not support full T-SQL and cannot query CSV or Parquet files sitting in Blob Storage. Using request-unit (RU) billing for ad-hoc, file-based analytics would also be both cost-prohibitive and technically incorrect for this scenario.

  • Azure Analysis Services

    Why it's wrong here

    Azure Analysis Services is an OLAP semantic modeling platform that composes tabular or multidimensional models from data that already exists elsewhere. It is not a query engine that can scan raw files directly; you must import or pull the historical sales data into a model before it can be surfaced to clients. Unlike a serverless SQL pool, it requires a defined model, a hosted service, and typically higher administrative effort, so it does not provide direct T-SQL read access to Blob Storage files.

Quick reference

Azure Blob Storage Tier Comparison

TierStorage CostRetrieval CostLatencyUse Case
HotHighestLowestImmediateActive data, frequent reads
CoolLowerHigherImmediateData accessed < once / month
ColdLower stillHigherImmediateData accessed < once / quarter
ArchiveLowestHighest + rehydration delayHoursLong-term compliance retention

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