DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
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.
Answer choices
Why each option matters
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.
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 Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store designed for semi-structured data at scale, but it only supports queries on the partition key and row key, or full table scans; it lacks indexing on arbitrary properties, aggregate functions, and join operations. While it can persist IoT data, it cannot efficiently execute analytical queries that filter or group by device properties or timestamps, making it unsuitable for fast historical analytics.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage provides massively scalable, cost-effective object storage for unstructured data such as JSON or CSV files, but it is a raw storage layer with no native query engine. To run analytics, you must copy data into a queryable service like Azure Synapse or Databricks, and even then, scanning blob contents incurs high latency and cost; it is optimized for storing data, not for serving fast analytic queries.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 combines hierarchical namespace with object storage, and unlike Blob Storage, it is deeply integrated with Azure Synapse, Databricks, and HDInsight, which can push down queries through its file system and execute distributed parallel processing. Its design supports schema-on-read, partitioning, and columnar formats like Parquet, enabling fast analytical queries directly on the same data without copying, making it ideal for large-scale historical IoT analytics.
- ✗
Azure Event Hubs
Why it's wrong here
Azure Event Hubs is a real-time streaming ingestion and event-processing service that captures telemetry at high throughput, but it is a time-bounded replay buffer, not a durable analytical store. Default retention expires data quickly (up to 7 days or 90 days with a feature), and it lacks SQL-like querying, aggregation, and joins on historical data; it is meant to feed downstream analytics, not serve as the system of record for long-term querying.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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