Quick Answer
The answer is Azure Data Lake Storage Gen2. This is the correct output sink because it combines a hierarchical namespace with Azure Blob Storage’s scalable object storage, enabling fast querying for historical analysis through tools like Azure Synapse Analytics, PolyBase, or Apache Spark. For the DP-900 exam, this question tests your understanding of which Azure storage service is optimized for analytical workloads rather than simple blob storage. A common trap is choosing Azure Blob Storage, but remember that Blob Storage lacks the hierarchical namespace needed for efficient directory-level operations and fine-grained access control at scale. The key differentiator is that ADLS Gen2 supports high-throughput writes from Stream Analytics while allowing fast, folder-based queries—critical for IoT data analytics. Memory tip: think “Gen2 for analytics” because the “2” stands for two key features—hierarchical namespace and Blob Storage scalability—that together make historical querying fast.
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.
A company uses Azure Stream Analytics to process IoT data from thousands of devices. They need to store the results in a way that supports fast querying for historical analysis. Which output sink should they use?
Azure Table Storage
Why wrong: No support for complex queries.
Azure Blob Storage
Why wrong: Can store but not optimized for fast analytic queries.
Azure Data Lake Storage Gen2
Designed for big data analytics and fast querying.
Azure Event Hubs
Why wrong: For real-time ingestion, not historical storage.
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 Data Lake Storage Gen2
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct output sink because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, enabling fast querying for historical analysis via tools like Azure Synapse Analytics, PolyBase, or Apache Spark. ADLS Gen2 supports high-throughput writes from Stream Analytics and allows efficient directory-level operations and fine-grained access control, which are critical for large-scale IoT data analytics.
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
No support for complex queries.
Azure Blob Storage
Why it's wrong here
Can store but not optimized for fast analytic queries.
Azure Data Lake Storage Gen2
Why this is correct
Designed for big data analytics and fast querying.
Related concept
Read the scenario before looking for a memorised answer.
Azure Event Hubs
Why it's wrong here
For real-time ingestion, not historical storage.
Common exam traps
The trap here is that candidates often confuse Azure Blob Storage with ADLS Gen2, assuming both are equivalent for analytics, but the key differentiator is the hierarchical namespace and native integration with big data analytics engines that ADLS Gen2 provides.
Detailed technical explanation
ADLS Gen2 uses a hierarchical namespace that allows file system semantics (e.g., atomic directory rename/delete) and POSIX-like permissions, which significantly improve performance for analytical queries that scan large datasets. Under the hood, Stream Analytics writes data in a partitioned format (e.g., by date/time) to ADLS Gen2, enabling efficient pruning during query execution. In a real-world scenario, a company processing 10,000 IoT device messages per second can use ADLS Gen2 to store data in Parquet format, reducing storage costs and query times by up to 90% compared to flat blob storage.
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 Data Lake Storage Gen2 — Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct output sink because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, enabling fast querying for historical analysis via tools like Azure Synapse Analytics, PolyBase, or Apache Spark. ADLS Gen2 supports high-throughput writes from Stream Analytics and allows efficient directory-level operations and fine-grained access control, which are critical for large-scale IoT data analytics.
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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