DP-900 Describe core data concepts Practice Question
Your company is designing a data solution for IoT sensor data that arrives in high volume and must be stored for long-term analytics. The data is append-only and rarely updated. You need to choose a storage solution that balances cost and query performance for historical analysis. Which Azure data store should you recommend?
⚠ Common exam trap
Many candidates confuse Azure Cosmos DB's low-latency capabilities with suitability for high-volume historical analytics, overlooking its cost model and lack of native file-system semantics for append-only workloads.
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 is the correct choice because it combines a hierarchical namespace with Azure Blob Storage, offering scalable, cost-effective storage for high-volume append-only data like IoT sensor logs. It supports both structured and unstructured data, integrates with analytics engines like Azure Synapse and Spark, and provides POSIX-compliant access control, making it ideal for long-term historical analysis at low cost.
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 Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed, multi-model NoSQL database engineered for OLTP patterns where sub-10-millisecond response times and always-on availability are mandatory. Its request-unit (RU) consumption model charges for every read and write, and the automatic indexing of all properties makes storing petabytes of append-only IoT history prohibitively expensive. Analytical queries that scan large time ranges compete with transactional traffic and are far less efficient than the parallel scans native to data lakes. Thus, despite low latency, it is the wrong fit for a historical IoT data archive.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store that offers very low storage cost and high scalability, but it exposes only a limited query model: lookups must specify a partition key and optionally a row key, with no secondary indexes. Sensor historian workloads that require time-range aggregations, joins, or arbitrary property filtering would need manual table scans and client-side processing, creating poor performance and complex application code. It also lacks native integration with distributed analytics engines like Azure Synapse pipelines in the same way that data lake storage supports PolyBase and notebook workloads. Therefore, it is unsuitable as the foundation for broad historical analytics.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational engine that provides ACID transactions, secondary indexes, and T-SQL querying, but these strengths come with a per-vCore and per-storage price that grows steeply as IoT telemetry accumulates. Separately, the append-only, high-ingest pattern of sensor data creates ongoing index fragmentation and checkpoint overhead, and the database imposes fixed size limits that force frequent partitioning or archival strategies. While you could denormalize a schema to make it work, the operational burden and licensing costs would dwarf those of an object-store-based lake. In short, it is an OLTP-oriented system, not a low-cost analytical archive.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 combines the massive, low-cost capacity of Azure Blob Storage with a hierarchical namespace and POSIX-style access control lists, making it ideal for storing raw and curated IoT data at petabyte scale. Append-only files are written sequentially without update-in-place costs, and because storage is decoupled from compute you can run serverless analytics or spin up Spark clusters only when needed. It integrates natively with Azure Synapse Analytics, Azure Databricks, and HDInsight, enabling schema-on-read processing over Parquet or Delta Lake files. For a historical IoT sensor archive, this is the correct foundation because it makes analytics practical and economical.
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
Unstructured data
Unstructured data is information that does not have a predefined data model or is not organized in a predefined manner, making it difficult for traditional databases to read and process.
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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