DP-203 Design and implement data storage Practice Question
Which TWO strategies can be used to optimize storage costs for historical data in Azure Data Lake Storage Gen2?
⚠ Common exam trap
Watch out — candidates often confuse data protection features (soft delete, encryption, replication) with cost optimization strategies, but only tiering and compression directly reduce the amount or cost of stored data.
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
✓
Implement lifecycle management policies to move data to archive tier
Option C is correct because Azure Storage lifecycle management policies can automatically transition blobs from hot/cool tiers to the archive tier after a defined age, and the archive tier has the lowest storage cost per GB for rarely accessed historical data. Option D is correct because storing historical data in a compressed columnar format such as Parquet reduces the total bytes stored and scanned, and columnar compression typically achieves much higher compression ratios than row-based formats, directly lowering storage and query costs in ADLS Gen2. Option A is not a cost-optimization strategy; soft delete retains deleted data and can actually increase storage charges during the retention period. Option B is not cost-optimizing either, since GRS replicates data to a secondary region and costs more than LRS. Option E is also not a cost strategy; Azure Storage Service Encryption is enabled by default and does not reduce storage cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable soft delete to recover data
Why it's wrong here
Soft delete preserves deleted blobs for a retention period, adding storage cost rather than reducing it. It is tempting because it protects against accidental deletion, which is its actual purpose, but the scenario requires lifecycle tiering to cooler access tiers or expiry-based deletion to lower historical data costs.
- ✗
Use geo-redundant storage (GRS) for durability
Why it's wrong here
GRS replicates data to a secondary region, raising cost rather than lowering it. It is tempting because durability sounds like good stewardship, but cost optimisation for historical data uses lifecycle management tiering to cool and archive access tiers.
- ✓
Implement lifecycle management policies to move data to archive tier
Why this is correct
Lifecycle management policies automatically transition blobs from hot to cool then archive tier based on age, cutting per-GB cost for historical data. This directly satisfies the storage cost optimisation requirement without manual intervention or data deletion.
- ✓
Store data in compressed columnar format like Parquet
Why this is correct
Parquet's columnar layout and built-in compression shrink stored bytes dramatically and let queries read only needed columns. This reduces both capacity charges and scan volume, directly satisfying the storage cost optimisation requirement for historical data.
- ✗
Encrypt data with Azure Storage Service Encryption
Why it's wrong here
Storage Service Encryption applies automatically to all data at rest at no extra charge, so it changes nothing about cost. It is tempting because encryption is a security baseline, but the cost-relevant strategies are lifecycle tiering to cool and archive storage.
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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