AZ-305 Design data storage solutions Practice Question
A multinational corporation needs to store and analyze petabytes of historical data for regulatory reporting. The data is rarely accessed but must be available for queries within 5 minutes. Which Azure storage solution should they choose to minimize cost?
⚠ Common exam trap
A common mix-up: candidates choose Azure SQL Database or Cosmos DB for 'query performance' without considering the massive cost and architectural mismatch for petabyte-scale cold data, or they pick Azure Files thinking 'file storage' implies analytical capability, ignoring its lack of native query engines and higher cost per GB.
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 choice because it combines a hierarchical namespace with Azure Blob Storage's massive scalability, enabling petabyte-scale storage at low cost. It supports fast queries via tools like Azure Synapse or PolyBase, meeting the 5-minute query SLA for cold data, while its tiered storage (e.g., Cool or Archive access tiers) minimizes cost for rarely accessed historical data.
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 SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database designed for transactional (OLTP) workloads, not petabyte-scale analytics. Even with Hyperscale or columnstore indexes, a petabyte of raw data requires massive sharding and leads to enormous licensing, compute, and storage costs. Its architecture prioritizes concurrency and consistency over the low-cost sequential reads needed for big data analytics, making it economically impractical for this scenario.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct foundation for petabyte-scale analytics because it combines Blob Storage's massive, low-cost capacity with a hierarchical namespace for folder-level permissions and efficient file management. It is engineered for high-throughput parallel scanning, and features like query acceleration allow filtering and aggregating large datasets without spinning up dedicated compute. Its native integration with Azure Synapse, Databricks, and HDInsight makes it the analytics data lake standard.
- ✗
Azure Files
Why it's wrong here
Azure Files provides SMB/NFS file shares primarily for virtual-machine file shares or lift-and-shift application workloads, not for massive analytical processing. It lacks the distributed data fabric, blob-tiering, and built-in query acceleration required for petabyte-scale scans. Production analytics would quickly exhaust its throughput/burst limits, and its per-GiB price at that scale becomes prohibitive with no analytical compute engine attached.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed, multi-model NoSQL database optimized for high-speed, low-latency transactional access (OLTP), not petabyte-scale offline analytics. Storing petabyte-sized datasets would require continuous high request-unit (RU) throughput, making warm storage prohibitively expensive. Although Cosmos DB has an analytical store, it is a secondary feature designed for near-real-time insights on operational data, not a replacement for a data lake in a centralized analytics platform.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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