AZ-204 Develop for Azure storage Practice Question
You are building a solution that uploads large files (up to 100 GB) to Azure Blob Storage. Users frequently experience timeout errors when uploading files over slow network connections. Which approach should you use to maximize reliability?
⚠ Common exam trap
Many exam-takers confuse AzCopy's checkpointing (Option D) as the only reliable method for large uploads, but the question specifies building a solution (SDK-based), not using a standalone tool, and AzCopy cannot be programmatically embedded in an application.
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
✓
Use the Azure Storage SDK to upload the file as a block blob with multiple parallel blocks and implement retry logic with exponential backoff.
Uploading a large file as a block blob with multiple parallel blocks maximizes throughput and reliability over slow networks. The Azure Storage SDK automatically splits the file into blocks (up to 100 MB each), uploads them concurrently, and implements retry logic with exponential backoff to handle transient failures. This approach is specifically designed for large file uploads and mitigates timeout errors by keeping individual block transfers small and resumable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Upload the file as a page blob in 512-byte chunks.
Why it's wrong here
Page blobs are designed for random read/write operations on fixed-size 512-byte pages, making them suitable for virtual hard disk (VHD) files where specific byte ranges are frequently updated. For large sequential file uploads, using page blobs in 512-byte chunks is inefficient and not their intended use case. This approach would incur significant overhead due to the page-oriented API, which is optimized for frequent, small, random updates rather than contiguous data streams, making block blobs the appropriate choice for this scenario.
- ✓
Use the Azure Storage SDK to upload the file as a block blob with multiple parallel blocks and implement retry logic with exponential backoff.
Why this is correct
This is the correct approach for uploading large files to Azure Storage. Block blobs are optimized for large, sequential data uploads, allowing files to be broken into independent blocks that can be uploaded in parallel, significantly improving throughput and reducing total upload time. The Azure Storage SDK inherently supports this parallelization and provides robust, configurable retry logic with exponential backoff, which is crucial for handling transient network issues and service throttling, ensuring reliable delivery of the entire file even under adverse conditions.
- ✗
Increase the client-side timeout value to 10 minutes.
Why it's wrong here
While increasing the client-side timeout might prevent premature connection drops for very slow uploads or large file transfers, it does not address the root cause of transient network or service-side failures. A longer timeout simply allows a single operation to take more time before failing completely. It offers no mechanism to automatically reattempt a failed operation, which is essential for robustly uploading large files over potentially unreliable networks. Implementing comprehensive retry logic is fundamental for building resilient solutions.
- ✗
Use AzCopy with the /Z parameter to enable checkpointing.
Why it's wrong here
AzCopy is a powerful command-line utility designed for high-performance data transfer to and from Azure Storage, and its `/Z` parameter enables checkpointing for resuming failed transfers. However, the question implies building a "solution" which typically refers to programmatic integration within application code. AzCopy is an external tool, not a library or SDK component for direct programmatic control within an application, making it unsuitable for an embedded, custom upload solution that requires code-level integration and control.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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