AZ-204 Develop for Azure storage Practice Question
You are building a data pipeline that writes billions of small log records (each ~200 bytes) to Azure Blob Storage. The logs are always written in chronological order and are read sequentially in order. You must minimize storage cost and achieve maximum write throughput. Which blob type should you use?
⚠ Common exam trap
Test-takers frequently choose Block blobs (Option A) thinking they are the default for any data, but they overlook the append-specific optimization and the overhead of block management for billions of small writes.
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
✓
Append blobs in the Hot tier
Append blobs are optimized for append operations, making them ideal for writing billions of small log records in chronological order. They support high-throughput sequential writes without the overhead of managing block IDs, and the Hot tier provides low-latency access for immediate reading, minimizing storage cost while maximizing write throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Block blobs in the Cool tier
Why it's wrong here
Block blobs are designed for storing large, discrete objects and are not optimized for frequent, small appends. Each append operation would effectively require downloading the existing blob, adding the new data, and then re-uploading the entire modified content as new blocks, followed by committing the block list. This process is highly inefficient and costly for a data pipeline writing billions of small log entries, leading to poor performance and increased transaction charges. The Cool tier, while cost-effective for infrequently accessed data, still incurs higher transaction costs than Hot for frequent writes.
- ✓
Append blobs in the Hot tier
Why this is correct
Append blobs are purpose-built for scenarios requiring efficient append operations, such as logging or streaming data, where new data is continuously added to the end of a blob without modifying existing content. They allow for fast, sequential writes, ensuring high throughput crucial for ingesting billions of small log entries. The Hot tier provides the lowest access latency and transaction costs, making it the most suitable choice for frequently written and immediately accessed data, optimizing both performance and operational cost for active log ingestion.
- ✗
Page blobs in the Premium tier
Why it's wrong here
Page blobs are optimized for random read/write operations on fixed-size 512-byte pages, primarily serving as the underlying storage for Azure Virtual Machine disks. This architecture is fundamentally unsuitable for sequential appending of variable-sized log data, as each log entry would require complex page management rather than a simple extension. The Premium tier, while offering extremely low latency and high IOPS, is significantly more expensive and provides capabilities far beyond the requirements of a log ingestion pipeline, making it an inefficient and costly choice for this use case.
- ✗
Block blobs in the Archive tier
Why it's wrong here
The Archive tier is designed for long-term, rarely accessed data storage, offering the lowest storage costs but with significant retrieval latency and costs. It is entirely incompatible with an active data pipeline that writes billions of small log entries, as it is not optimized for frequent write operations or immediate data availability. Attempting to use the Archive tier for active logging would incur prohibitive transaction costs, introduce unacceptable delays for data ingestion, and make the data practically inaccessible for real-time analysis, rendering it unsuitable for any active write 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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