Quick Answer
The answer is Azure Blob Storage and Azure Data Lake Storage Gen2, as both services are designed to store semi-structured data like JSON or Parquet files for analytics. Azure Blob Storage provides highly scalable, cost-effective object storage that natively supports these formats, while Azure Data Lake Storage Gen2 builds on Blob Storage by adding a hierarchical namespace and POSIX-like access controls, making it ideal for big data analytics workloads. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of how Azure’s storage services map to data types—specifically that semi-structured data, unlike fully structured relational data, belongs in object or data lake storage rather than Azure SQL Database. A common trap is confusing Azure Cosmos DB (which also stores JSON) as the primary analytics store, but remember: Cosmos DB is for transactional, low-latency access, not large-scale analytics. For the exam, think “Blob and Data Lake Gen2 = analytics lake for JSON and Parquet.”
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.
Which TWO Azure services can be used to store semi-structured data like JSON or Parquet files for analytics? (Choose two.)
Azure Synapse Analytics
Why wrong: Data warehouse, not storage.
Azure Blob Storage
Object storage, can store any file type.
Azure SQL Database
Why wrong: Relational database, not optimized for files.
Azure Data Lake Storage Gen2
Hierarchical namespace, supports any file format.
Azure Cosmos DB
Why wrong: NoSQL database, not a file store.
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 Blob Storage
Azure Blob Storage is a highly scalable, cost-effective object storage service that can store any type of unstructured data, including semi-structured formats like JSON and Parquet. It is commonly used as a data lake for analytics workloads, where raw or processed data is stored and later queried by services like Azure Synapse Analytics or Azure Databricks.
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 Synapse Analytics
Why it's wrong here
Data warehouse, not storage.
Azure Blob Storage
Why this is correct
Object storage, can store any file type.
Related concept
Read the scenario before looking for a memorised answer.
Azure SQL Database
Why it's wrong here
Relational database, not optimized for files.
Azure Data Lake Storage Gen2
Why this is correct
Hierarchical namespace, supports any file format.
Related concept
Read the scenario before looking for a memorised answer.
Azure Cosmos DB
Why it's wrong here
NoSQL database, not a file store.
Common exam traps
The trap here is that candidates often confuse Azure Synapse Analytics (a query service) with a storage service, or think Azure Cosmos DB is suitable for storing large Parquet files for analytics, when it is actually a transactional NoSQL database with a different cost and performance profile.
Detailed technical explanation
Azure Blob Storage and Azure Data Lake Storage Gen2 both support hierarchical namespace (HNS) for organizing files, but Data Lake Storage Gen2 adds POSIX-like access control and directory-level operations, making it ideal for big data analytics with tools like Apache Spark. Under the hood, both services store data as blobs in containers, but Data Lake Storage Gen2 uses a flat namespace with a virtual directory structure, enabling efficient parallel processing of Parquet files in scenarios like ETL pipelines or machine learning training.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 Blob Storage — Azure Blob Storage is a highly scalable, cost-effective object storage service that can store any type of unstructured data, including semi-structured formats like JSON and Parquet. It is commonly used as a data lake for analytics workloads, where raw or processed data is stored and later queried by services like Azure Synapse Analytics or Azure Databricks.
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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