AZ-204 Develop for Azure storage Practice Question
You are designing a solution that writes millions of small log records (each 200 bytes) to Azure Blob Storage. The logs are written every second, always appended to a single file. The file must be read periodically by a batch process that reads the entire file. You need to maximize write throughput and minimize storage costs. Which blob type and access strategy should you choose?
⚠ Common exam trap
Many exam-takers choose Block blobs (Option A) thinking they can append data by adding new blocks, but they overlook the inefficiency of the block list management and the lack of native append support, which makes Append blobs the correct choice for sequential append workloads.
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 Append blobs and write each log entry as an append block
Append blobs are optimized for append operations, making them ideal for writing millions of small log records sequentially to a single file. Data is written in append blocks. For millions of small log entries, these entries would typically be buffered and written together as larger append blocks (up to 4 MB each) to optimize performance and and stay within the append blob's 50,000 block limit. This provides high throughput for append-heavy workloads and minimizes storage costs by storing data in a single blob.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Block blobs and append the data to a single blob
Why it's wrong here
Using Block blobs to append data to a single blob is highly inefficient for millions of small log records. Block blobs are designed for large, discrete objects or updates to specific blocks, not for frequent, small appends to the end of an existing file. Each "append" operation would necessitate managing a block list, potentially re-uploading existing data, or committing a new block list, leading to excessive transaction costs and poor performance for a high-volume logging scenario.
- ✓
Use Append blobs and write each log entry as an append block
Why this is correct
Append blobs are the optimal choice for writing millions of small log records because they are specifically engineered for sequential append operations. Each log entry can be written as an individual append block, efficiently adding data to the end of the blob without modifying existing content. This design ensures high throughput, low latency, and cost-effectiveness for continuous data streams like application logs, making them ideal for this workload.
- ✗
Use Page blobs and write each log entry to a page
Why it's wrong here
Page blobs are fundamentally unsuitable for append-only logging workloads due to their design and cost structure. Optimized for random read/write operations on fixed 512-byte pages, they are primarily used for virtual machine disks. Writing millions of small log entries to individual pages would incur significantly higher transaction costs and management overhead compared to append blobs, as their performance characteristics are not aligned with sequential data ingestion.
- ✗
Use Block blobs and create a new blob for each log entry
Why it's wrong here
Creating a new Block blob for each of millions of small log entries is an anti-pattern that introduces severe management and performance overhead. This approach would result in an enormous number of tiny blobs, drastically increasing transaction costs for creation and management operations. Furthermore, listing, querying, and maintaining such a vast collection of individual objects would significantly reduce overall write throughput and increase operational complexity.
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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