DP-203 Design and implement data storage Practice Question
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 JSON data from IoT sensors. The data is ingested continuously and must support both real-time analytics and batch processing. Which Azure data store should you recommend?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Lake Storage Gen2
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical file system with the scalability and low cost of Azure Blob Storage, making it ideal for storing semi-structured JSON data from IoT sensors. It supports both real-time analytics (via services like Azure Stream Analytics or Apache Spark) and batch processing (via tools like Azure Data Factory or PolyBase) without data movement, and it natively handles JSON files with schema-on-read capabilities.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Lake Storage Gen2
Why this is correct
ADLS Gen2 combines Blob Storage with a hierarchical namespace and is designed for big data analytics.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Blob Storage
Why it's wrong here
Blob Storage lacks a hierarchical namespace and integrated analytics optimizations.
✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is relational and not ideal for storing semi-structured JSON at large scale for analytics.
✗
Azure Cosmos DB
Why it's wrong here
Cosmos DB is a NoSQL database optimized for low-latency transactional workloads, not for analytics at scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Azure Cosmos DB (Option D) because it natively supports JSON and real-time access, but they overlook the requirement for batch processing and cost-effective storage at scale, which ADLS Gen2 is designed for as a data lake solution.
Detailed technical explanation
How to think about this question
ADLS Gen2 uses a hierarchical namespace that maps POSIX-like access control lists (ACLs) to directories and files, enabling efficient rename and delete operations without scanning all blobs. Under the hood, it stores data as blobs but exposes a file system abstraction via the ABFS driver (Azure Blob File System), which allows tools like Apache Spark to read and write JSON directly without data conversion. In a real-world IoT scenario, raw sensor JSON can be ingested into ADLS Gen2 partitions (e.g., by date/sensor), then processed in real-time with Spark Structured Streaming for dashboards and later batch-processed with Azure Synapse Analytics for historical reporting, all from the same data lake.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Data Lake Storage Gen2 — Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical file system with the scalability and low cost of Azure Blob Storage, making it ideal for storing semi-structured JSON data from IoT sensors. It supports both real-time analytics (via services like Azure Stream Analytics or Apache Spark) and batch processing (via tools like Azure Data Factory or PolyBase) without data movement, and it natively handles JSON files with schema-on-read capabilities.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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