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Describe an analytics workload on AzurehardMultiple ChoiceObjective-mapped

DP-900 Describe an analytics workload on Azure Practice Question

An organization has a large dataset stored in Azure Blob Storage. They need to run complex analytics using SQL queries and also want to use the same data for machine learning models. Which Azure service provides both SQL-based analytics and native integration with ML frameworks?

⚠ Common exam trap

A common mix-up: candidates confuse Azure Synapse Analytics with Azure SQL Database or Azure Data Factory, mistakenly thinking a traditional database or an ETL tool can handle both complex SQL analytics and native ML integration on large-scale Blob Storage data.

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

Azure Synapse Analytics is correct because it provides a unified analytics platform that combines enterprise data warehousing with big data analytics. It offers built-in SQL-based querying via dedicated SQL pools or serverless SQL pools, and it natively integrates with machine learning frameworks like Apache Spark and Azure Machine Learning for building and training models on the same data stored in Azure Blob Storage.

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 Data Factory

    Why it's wrong here

    Azure Data Factory is a cloud-based ETL and data integration service that orchestrates data movement and transformation pipelines. While it can copy data from Azure Blob Storage to a variety of sinks, it does not provide interactive query, distributed SQL processing, or built-in machine learning capabilities for analyzing large datasets directly. It is a tool for building pipelines, not an analytics engine, so it would not satisfy the requirement for large-scale data analysis.

  • Azure Synapse Analytics

    Why this is correct

    Azure Synapse Analytics is a unified analytics platform that converges SQL data warehousing, big data processing (Apache Spark), and integrated machine learning under one service. It can query data directly from Azure Blob Storage using serverless SQL or Spark pools, enabling large-scale analytics without managing infrastructure. Its tight integration with Azure Machine Learning and Power BI makes it the appropriate choice for analyzing large datasets stored in blob storage.

  • Azure Analysis Services

    Why it's wrong here

    Azure Analysis Services provides enterprise-grade semantic models and tabular modeling for business intelligence, allowing users to define measures, KPIs, and hierarchies. However, it is not a data processing or ML engine; it consumes data that has already been prepared and aggregated, typically from a data warehouse. It lacks native connectivity to perform heavy computations directly on raw files in Azure Blob Storage and does not incorporate machine learning capabilities.

  • Azure SQL Database

    Why it's wrong here

    Azure SQL Database is a fully managed relational database optimized for online transaction processing (OLTP). It is not designed for large-scale analytical queries, which require distributed computing and columnar or MPP (massively parallel processing) architectures. While you could load the data into SQL Database, it would struggle with petabyte-scale datasets and lacks the integrated machine learning and big data runtime found in Synapse, making it unsuitable for this large-scale analytics scenario.

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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Last reviewed: Jun 24, 2026

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