AZ-204 Develop for Azure storage Practice Question
You need to store billions of small telemetry data entries (each ~100 bytes) from IoT devices. The data is written once and rarely updated. You need to run analytical queries on the last 30 days of data daily. The queries scan large ranges of data by timestamp and require sub-second response times. You need the lowest storage cost while meeting query latency requirements. Which Azure Storage solution should you use?
⚠ Common exam trap
A common mix-up: candidates choose Azure Cosmos DB (Option C) for its low-latency queries, overlooking that its cost model (RU/s) makes it prohibitively expensive for scanning billions of small records, while Blob Storage with Data Lake Storage Gen2 provides the required performance at a fraction of the cost when using proper partitioning and file formats.
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 Blob Storage with hot access tier and Data Lake Storage Gen2.
Azure Blob Storage with the hot access tier is suitable for data that is accessed frequently (daily queries on the last 30 days), providing lower transaction costs compared to the cool tier for active data. Azure Data Lake Storage Gen2, built on Blob Storage, enables hierarchical namespace and POSIX-like access, allowing efficient analytical queries on large timestamp-ranged data with sub-second response times via partitioning and parallel processing, meeting the performance and scalability requirements at a cost-effective price point for the storage of billions of small entries.
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 hot access tier and Data Lake Storage Gen2.
Why this is correct
Blob Storage with Data Lake Storage Gen2 lacks native indexing for sub-second range scans over timestamped data, requiring full file scans that cannot meet the query latency requirement. It is tempting because its hot tier offers low-cost bulk storage for immutable telemetry, and it would be correct for archival or batch-processing workloads where sub-second analytical response is not demanded.
- ✗
Azure Table Storage with a timestamp partition key.
Why it's wrong here
Azure Table Storage is a NoSQL key-value store optimized for high-volume, structured data with point lookups using PartitionKey and RowKey. While it can store billions of entities, using a timestamp solely as a PartitionKey would lead to hot partitions or very wide partitions, making range scans across large time periods inefficient and slow. Its design is not suited for complex analytical queries over large datasets, especially when sub-second response times are required for timestamp ranges.
- ✗
Azure Cosmos DB with SQL API and automatic indexing.
Why it's wrong here
Azure Cosmos DB with the SQL API offers excellent low-latency query capabilities and automatic indexing, making it technically capable of handling timestamp range queries efficiently. However, for storing billions of small, append-only telemetry entries that are primarily queried daily, Cosmos DB's Request Unit (RU) consumption model makes it significantly more expensive than object storage solutions. It is optimized for transactional workloads requiring guaranteed low latency and high throughput, not for cost-effective bulk analytical storage at this scale.
- ✗
Azure Blob Storage with cool access tier and Azure Data Lake Storage Gen2.
Why it's wrong here
Azure Data Lake Storage Gen2, built on Azure Blob Storage, provides a hierarchical namespace that is crucial for organizing billions of telemetry entries, typically by timestamp (e.g., year/month/day/hour). This structure enables efficient data pruning and predicate pushdown when integrated with analytical engines, allowing for faster range scans. The "cool" access tier is ideal for data queried daily but not constantly, offering a cost-effective balance between storage and access costs for this high-volume, append-only dataset, making it suitable for analytical workloads.
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
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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.