AZ-204 Develop for Azure storage Practice Question
You store application logs in Azure Blob Storage. The logs are accessed frequently for the first 7 days, then rarely. After 30 days, they must be deleted to minimize cost. Which approach should you use?
⚠ Common exam trap
The trap here is that candidates may overcomplicate the solution by choosing manual scripting (A) or a heavy orchestration tool (D), missing that Azure provides a native, policy-driven mechanism (lifecycle management) specifically designed for automated tiering and deletion based on age.
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
✓
Configure a lifecycle management policy to tier to cool after 7 days and delete after 30 days
Azure Blob Storage lifecycle management policies allow you to automatically transition blobs to a cooler tier (cool) after a specified number of days and then delete them after another period, all without manual intervention or additional services. This directly meets the requirement of frequent access for 7 days, rare access afterward, and deletion at 30 days, minimizing cost by leveraging tiered storage and automated rules.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually move blobs to cool tier after 7 days and delete after 30 days using a script
Why it's wrong here
Manually moving blobs to a cool tier after 7 days and deleting them after 30 days using a script is an inefficient and error-prone approach. This method requires custom development, deployment, and ongoing maintenance of a compute resource (e.g., Azure Function or VM) to execute the script, adding significant operational overhead. Furthermore, custom scripts lack the inherent reliability, scalability, and built-in monitoring that Azure's native lifecycle management services provide for age-based data transitions.
- ✗
Use blob snapshots and delete snapshots after 30 days
Why it's wrong here
Using blob snapshots and deleting them after 30 days is not suitable for managing access tiers or automated deletion of primary log data based on age. Snapshots create point-in-time, read-only versions of a base blob, primarily for data recovery or versioning, and incur storage costs for the delta changes. They do not automatically transition the base blob's access tier, nor do they provide a mechanism to automatically delete the original blob based on its age, making this an inappropriate and costly solution for log lifecycle management.
- ✓
Configure a lifecycle management policy to tier to cool after 7 days and delete after 30 days
Why this is correct
Configuring a lifecycle management policy directly addresses the requirements by providing an automated, cost-effective, and serverless solution. These policies allow defining rules that automatically transition blobs between access tiers (e.g., Hot to Cool after 7 days) and delete them (e.g., after 30 days) based on conditions like blob age or last modified time. This native Azure Blob Storage feature eliminates manual scripting, reduces operational overhead, and optimizes storage costs by ensuring data resides in the most appropriate tier.
- ✗
Use Azure Data Factory to copy old logs to archive storage and delete original
Why it's wrong here
Azure Data Factory is a data integration and orchestration service, not a lifecycle management tool; it lacks native capability to enforce a 30-day deletion policy based on blob age. This option is tempting because Data Factory can copy blobs to archive tier, which seems to address the "delete after 30 days" requirement, but it would require building custom pipeline logic and scheduled triggers to manage deletion, whereas Azure Blob Storage lifecycle management policies automatically delete blobs when the specified age condition is met.
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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