AZ-204 Develop for Azure storage Practice Question
An application stores large media files (up to 5 GB) that are frequently appended to but rarely read sequentially. Which Azure Blob Storage type should be used to optimize writes and cost?
⚠ Common exam trap
Many candidates choose block blobs because they are the default and most familiar type for large files, overlooking that append blobs are specifically designed for frequent append operations and offer better write performance and cost efficiency for that pattern.
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 blob
Append blobs are optimized for append operations, making them ideal for scenarios like logging or storing media files that are frequently appended to. They support high-throughput writes without the overhead of managing block lists, and they are cost-effective for sequential append workloads compared to block blobs, which require explicit block management and are better suited for random read/write patterns.
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 blob
Why it's wrong here
Block blobs are optimized for storing large, discrete objects like documents, images, and videos, where the entire object is typically uploaded or downloaded. While they can handle files up to 4.75 TB, frequent append operations are highly inefficient. Each append would require listing existing blocks, uploading new blocks, and then committing the entire block list again, or even re-uploading modified blocks, which is not designed for high-throughput sequential writes.
- ✓
Append blob
Why this is correct
Append blobs are specifically engineered for scenarios that require efficient, sequential write operations, making them ideal for logging, auditing, or, in this case, frequently appended media files. Each new write operation adds data to the end of the blob in an atomic manner, ensuring data integrity without modifying existing blocks. This design provides high performance for continuous data streams and supports files up to 195 GB, easily accommodating the 5 GB requirement.
- ✗
Page blob
Why it's wrong here
Page blobs are optimized for random read/write operations on fixed-size 512-byte pages, making them the underlying storage for Azure Virtual Machine disks. They are designed for scenarios where specific byte ranges within a file are frequently updated, not for sequential appends where data is continuously added to the end. Using page blobs for sequential appends would be inefficient and complex, requiring manual management of page boundaries and potentially overwriting data.
- ✗
Archive blob
Why it's wrong here
The Archive access tier is designed for storing rarely accessed, long-term data, offering the lowest storage costs but with the highest access latency and retrieval costs. It is explicitly not intended for frequent write or append operations. Data in the Archive tier must first be rehydrated to a hot or cool tier before it can be read or modified, making it entirely unsuitable for an application requiring frequent appends to media files.
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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