AZ-204 Develop for Azure storage Practice Question
You are designing a solution to ingest billions of small IoT sensor messages (each ~500 bytes). Messages arrive at high velocity and must be retained for 90 days. You need to query the data efficiently by device ID and timestamp. You want to minimize storage cost and write latency. Which Azure Storage solution should you use?
⚠ Common exam trap
Watch out — candidates often choose Azure Blob Storage (Option A) because it's commonly used for log storage, but they overlook that querying billions of small blobs by device ID and timestamp is inefficient without additional indexing services like Azure Data Lake or Cosmos DB, whereas Table Storage provides native, low-latency querying via its composite key structure.
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 Table Storage
Azure Table Storage is ideal for this scenario because it provides a cost-effective, schema-less NoSQL store that supports high-volume ingestion of billions of small messages with low write latency. Its partition key (device ID) and row key (timestamp) design enables efficient point queries by device and time range, while the 90-day retention aligns with Table Storage's lifecycle management capabilities.
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 Blob Storage with JSON logs
Why it's wrong here
Azure Blob Storage is designed for storing massive amounts of unstructured data, such as images, videos, or large log files. While it can store JSON documents, querying individual sensor readings within these blobs would necessitate downloading and parsing entire files, which is highly inefficient and costly for billions of small, frequently queried IoT messages. It lacks the native indexing and query capabilities required for granular, per-record access patterns typical of IoT telemetry.
- ✗
Azure Queue Storage
Why it's wrong here
Azure Queue Storage is a messaging service primarily used for transient, asynchronous message passing between application components, enabling decoupled processing. It is not designed for long-term data retention or complex querying of historical data. Messages have a configurable time-to-live (TTL) and are typically deleted after consumption, making it unsuitable for persistent storage and analytical queries on billions of sensor readings.
- ✓
Azure Table Storage
Why this is correct
Table Storage is optimized for storing large numbers of structured entities. Using device ID as partition key and timestamp as row key allows efficient point queries and range queries, with low write latency and cost.
- ✗
Azure File Storage
Why it's wrong here
Azure File Storage provides fully managed file shares in the cloud, accessible via industry-standard Server Message Block (SMB) or Network File System (NFS) protocols. It is designed for traditional file system scenarios, such as lift-and-shift applications or shared configuration files. This service is not engineered for high-volume ingestion of small, structured data records or for programmatic querying of individual entities, lacking the necessary APIs and performance characteristics for IoT telemetry.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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