AZ-204 Develop for Azure storage Practice Question
You are developing an application that writes log entries to Azure Blob Storage. Each log entry is approximately 500 bytes, and you expect to generate millions of entries per day. The logs are rarely read, and when they are read, you need to retrieve ranges of logs sequentially. Which blob type should you use to minimize storage costs and maximize write throughput?
⚠ Common exam trap
It's easy for candidates to choose block blobs because they are the default and most familiar blob type, overlooking that append blobs are specifically designed for append-heavy workloads like logging and provide better write throughput without block management overhead.
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
Append blobs are optimized for append operations, making them ideal for write-heavy, sequential logging scenarios. Each append operation adds data to the end of the blob, achieving high write throughput without the overhead of managing block lists. Since logs are rarely read and accessed sequentially, append blobs minimize storage costs compared to block blobs (which require block management overhead) and page blobs (which are designed for random access and are more expensive).
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
Why it's wrong here
Block blobs are designed for storing large, discrete objects and are optimized for high-throughput reads and writes of entire blocks, or for replacing specific blocks within a blob. While they can technically be used for appending by downloading, modifying, and re-uploading, or by managing block lists, this process is highly inefficient and expensive for frequent, small log entries. Each append would incur significant transaction overhead and latency, making them unsuitable for continuous, high-volume logging scenarios.
- ✓
Append blobs
Why this is correct
Append blobs are purpose-built for append operations, making them the ideal choice for logging and other data streaming scenarios where new data is continuously added to the end of a file. They allow new blocks of data to be committed sequentially to the end of the blob without modifying existing content, ensuring high write throughput and low cost per write transaction. This design provides a robust and efficient mechanism for maintaining a chronological record of events.
- ✗
Page blobs
Why it's wrong here
Page blobs are optimized for random read/write operations of fixed-size 512-byte pages, primarily used for Azure Virtual Machine disks. Their architecture is designed to support frequent, small, random I/O operations, not sequential appends of variable-sized log entries. Using page blobs for logging would be highly inefficient due to the overhead of managing page boundaries for each entry and their cost model, which is optimized for disk operations rather than high-volume sequential writes.
- ✗
Azure Files shares
Why it's wrong here
Azure Files shares use the SMB protocol and are designed for shared file access across virtual machines, not for high-throughput sequential blob writes. The per-file overhead and protocol latency would severely degrade write throughput for millions of small 500-byte entries, and the cost model (based on provisioned capacity or transaction tiers) is far higher than block blob storage for this volume. This option is tempting because Azure Files provides a familiar network drive interface, making it a natural choice for applications that need to mount a file system and append logs as files, which would be correct for scenarios requiring concurrent file sharing or legacy application compatibility.
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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