This DP-203 practice question tests your understanding of design and implement data storage. 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.
You are designing a data storage solution for a retail company that needs to store semi-structured IoT sensor data from thousands of devices. The data is ingested in near real-time, and queries will involve filtering by device ID and timestamp. The solution must minimize storage costs while supporting interactive queries. Which TWO Azure data storage options are most appropriate?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Azure Table Storage
Why wrong: Suited for structured key-value data.
Azure Cosmos DB
Why wrong: Optimized for low-latency NoSQL access, not cost-effective bulk storage.
Azure Blob Storage
Cost-effective for large volumes of semi-structured data.
Azure Data Lake Storage Gen2
Optimized for analytics and interactive queries.
Azure SQL Database
Why wrong: Relational, not ideal for semi-structured IoT data at scale.
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 (C) is correct because it provides a cost-effective, scalable object store for semi-structured IoT data, supporting near real-time ingestion via REST APIs or SDKs and enabling interactive queries through Azure Data Lake Storage Gen2's hierarchical namespace and integration with query engines like Azure Synapse Serverless SQL or PolyBase. Azure Data Lake Storage Gen2 (D) is correct as it builds on Blob Storage with a hierarchical namespace, optimized for analytics workloads and interactive queries using tools like Azure Synapse or Databricks, while minimizing costs through tiered storage and lifecycle management.
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 Table Storage
Why it's wrong here
Suited for structured key-value data.
Azure Cosmos DB
Why it's wrong here
Optimized for low-latency NoSQL access, not cost-effective bulk storage.
Azure Blob Storage
Why this is correct
Cost-effective for large volumes of semi-structured data.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Lake Storage Gen2
Why this is correct
Optimized for analytics and interactive queries.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Azure SQL Database
Why it's wrong here
Relational, not ideal for semi-structured IoT data at scale.
Common exam traps
The trap here is that candidates often choose Azure Cosmos DB for its low-latency querying capabilities, overlooking that the question emphasizes minimizing storage costs for large volumes of semi-structured IoT data, where object storage (Blob/Data Lake) is far cheaper and still supports interactive queries via analytics engines.
Detailed technical explanation
Azure Blob Storage supports append blobs for efficient streaming writes, ideal for IoT sensor data that arrives sequentially, and can be queried interactively using Azure Synapse Serverless SQL or Spark via the hierarchical namespace of Data Lake Storage Gen2, which enables directory-level operations and ACLs. Under the hood, Data Lake Storage Gen2 uses a flat namespace with a hierarchical file system abstraction, allowing partition pruning and predicate pushdown for queries filtering by device ID and timestamp, reducing scan costs. In real-world scenarios, a retail company might store raw IoT data in Blob Storage as Parquet files partitioned by date and device ID, then use Azure Synapse to run interactive queries without moving data, achieving both low cost and performance.
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
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Blob Storage — Azure Blob Storage (C) is correct because it provides a cost-effective, scalable object store for semi-structured IoT data, supporting near real-time ingestion via REST APIs or SDKs and enabling interactive queries through Azure Data Lake Storage Gen2's hierarchical namespace and integration with query engines like Azure Synapse Serverless SQL or PolyBase. Azure Data Lake Storage Gen2 (D) is correct as it builds on Blob Storage with a hierarchical namespace, optimized for analytics workloads and interactive queries using tools like Azure Synapse or Databricks, while minimizing costs through tiered storage and lifecycle management.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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