AZ-305 Design data storage solutions Practice Question
A company runs a data analytics workload that processes large amounts of unstructured data (images and videos). The data is accessed frequently for the first month, then rarely. They need to store the data cost-effectively for 7 years to meet compliance. The solution must support fast retrieval of data within the first month. Which Azure storage solution should they recommend?
⚠ Common exam trap
Watch out — candidates often confuse 'premium' with 'fast retrieval' and overlook that the hot tier already provides low-latency access for frequently used data, while premium is overkill and cost-prohibitive for this workload.
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 tier for 30 days, then lifecycle management to cool tier for 6 months, then archive tier
Azure Blob Storage with hot tier for the first 30 days meets the fast retrieval requirement for frequently accessed data, while lifecycle management automatically moves data to cool tier for 6 months and then to archive tier for the remaining 7-year compliance period, minimizing cost. The archive tier offers the lowest storage cost for rarely accessed data, and lifecycle policies ensure seamless transitions without manual intervention.
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 tier for 30 days, then lifecycle management to cool tier for 6 months, then archive tier
Why this is correct
This design correctly aligns storage cost with data access patterns over time. During the first 30 days the data is actively processed, so the hot tier's low latency and high throughput are appropriate; lifecycle management then automatically transitions blobs to the cool tier for 6 months, reducing base storage cost while still allowing analytical reads. After that period, moving to the archive tier provides the cheapest per-gigabyte storage for long-term retention, with retrieval latency acceptable for rarely accessed datasets. Azure Blob Storage scales to massive amounts of unstructured data, making this a cost-effective and operationally efficient lifecycle strategy.
- ✗
Azure Blob Storage with premium tier for 30 days, then lifecycle to archive tier
Why it's wrong here
Premium tier for blob storage is built for low-latency, high-IOPS workloads, typically with transaction costs several times higher than the hot tier and a storage price that is not justified for bulk analytics over images or videos. Running the first 30 days on premium tier would incur significant unnecessary expense, since the analytics workload does not need sub-10-millisecond blob access. Even if lifecycle management could transition these blobs to archive afterward, the premium period alone makes the solution far more expensive than using the hot tier, and the premium tier's performance guarantees provide no real benefit for offline data processing.
- ✗
Azure Files with lifecycle management
Why it's wrong here
Azure Files is a managed file share service designed for SMB and NFS access, commonly used for lifting-and-shifting file servers or sharing files across VMs, not for storing raw, large-scale unstructured datasets like images and videos. Although Azure Files offers share-level access tiers, its lifecycle management capabilities are not equivalent to Blob Storage's per-blob tier transitions and are not designed for high-throughput, REST-based analytics pipelines. Object storage in Blob Storage provides the native API support, massive scale, and cost-controlled tiering needed for a data analytics workload, so Azure Files is the wrong fit.
- ✗
Azure Disk Storage with snapshots
Why it's wrong here
Azure Disk Storage provides block-level persistent storage that must be attached to a virtual machine and is intended for operating system disks and data disks, not as a general-purpose repository for raw analytics data. Snapshots capture point-in-time copies of managed disks and are primarily a backup and disaster-recovery mechanism, with costs that scale with disk size and accrued incremental blocks. Using disk snapshots to retain large unstructured datasets would be prohibitively expensive, and the data would not be directly accessible by analytics services without first mounting disks, making this option unsuitable for this workload.
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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Senior Network & Security Engineer · founder of Courseiva
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