Quick Answer
The answer is Azure Synapse Analytics. This service is the correct choice because it provides a unified analytics platform that combines enterprise data warehousing with big data analytics, allowing you to run complex SQL queries on data stored in Azure Blob Storage while also natively integrating with machine learning frameworks like Apache Spark and Azure Machine Learning for model training on the same dataset. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of how Synapse bridges the gap between traditional SQL analytics and modern ML workloads, often appearing as a scenario where you must identify a single service for both tasks. A common trap is choosing Azure Databricks for ML or Azure SQL Database for SQL alone, but Synapse uniquely offers both. Memory tip: think "Synapse" as the synapse between SQL and ML—it connects the two worlds seamlessly.
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Azure Data Factory
Why wrong: ETL and data integration only.
Azure Synapse Analytics
Unified analytics platform with SQL and ML.
Azure Analysis Services
Why wrong: Semantic modeling, not ML integration.
Azure SQL Database
Why wrong: Transactional database, not designed for large-scale analytics.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
ETL and data integration only.
Azure Synapse Analytics
Why this is correct
Unified analytics platform with SQL and ML.
Related concept
Read the scenario before looking for a memorised answer.
Azure Analysis Services
Why it's wrong here
Semantic modeling, not ML integration.
Azure SQL Database
Why it's wrong here
Transactional database, not designed for large-scale analytics.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
Azure Synapse Analytics uses a distributed query engine (PolyBase) to query data in Azure Blob Storage via T-SQL, enabling complex analytics without moving data. Its Spark pools allow direct integration with ML libraries like PyTorch or TensorFlow, and the Synapse pipeline can orchestrate both SQL and ML workflows. A real-world scenario is a retail company analyzing petabytes of sales data in Blob Storage using serverless SQL for ad-hoc queries and then training a demand forecasting model in the same Synapse workspace.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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