AZ-204 Develop for Azure storage Practice Question
Your IoT solution generates billions of small telemetry entries (each ~100 bytes). Data is written once and rarely updated. You need to run analytical queries on the last 30 days of data daily, scanning large ranges by timestamp, requiring sub-second response times. You want the lowest storage cost. Which Azure Storage solution should you use?
⚠ Common exam trap
Test-takers frequently choose Azure Cosmos DB for its low-latency queries, overlooking the explicit 'lowest storage cost' requirement, which Table Storage satisfies due to its simpler architecture and lack of provisioned throughput costs.
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 correct because it is a NoSQL key-value store optimized for high-volume, low-cost storage of structured data like telemetry entries. It supports efficient range queries on the partition key (e.g., timestamp) and row key, enabling sub-second scans of large date ranges. Its storage cost is the lowest among Azure storage options for this workload, as it charges only for consumed capacity with no minimum throughput commitments.
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
Why it's wrong here
While Azure Blob Storage can store vast amounts of data, it treats each telemetry entry as an opaque blob. Retrieving and querying billions of small, structured telemetry entries efficiently for analytical purposes would require downloading and parsing entire blobs, leading to high latency and computational overhead, making it unsuitable for fast, targeted queries on individual structured records.
- ✓
Azure Table Storage
Why this is correct
Azure Table Storage is a highly scalable and cost-effective NoSQL key-value store specifically designed for storing massive amounts of structured, non-relational data. Its architecture allows for efficient storage and retrieval of billions of small telemetry entries, leveraging PartitionKey and RowKey for rapid lookups and range queries, which is crucial for time-series data analysis without the overhead of a full relational database or higher-cost NoSQL alternatives.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB provides globally distributed, multi-model NoSQL capabilities with guaranteed low latency and high throughput. While it could certainly store telemetry, its advanced features like global distribution, multiple APIs (SQL, MongoDB, Cassandra, Gremlin, Table), and guaranteed single-digit millisecond latency come at a significantly higher operational cost compared to Azure Table Storage, making it an over-provisioned and uneconomical choice for a scenario primarily focused on cost-effective storage and basic range queries of billions of small telemetry entries.
- ✗
Azure Data Lake Storage
Why it's wrong here
Azure Data Lake Storage (ADLS) is optimized for storing and processing petabytes of data, primarily large files, for big data analytics workloads. It excels at batch processing and analytical queries over massive datasets, often in conjunction with services like Azure Synapse Analytics or Databricks. However, ADLS is not designed for low-latency, sub-second retrieval or querying of individual small telemetry entries, as its file system-like nature introduces overhead for managing billions of tiny files or parsing large files to extract specific records.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-204 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-204 exam.