AZ-204 Develop for Azure storage Practice Question
You are building a solution that needs to process large CSV files uploaded to Azure Blob Storage. Each file can be up to 1 GB. You want to minimize processing time and cost. Which approach should you recommend?
⚠ Common exam trap
Many candidates choose Logic Apps or Stream Analytics because they are 'serverless' and 'low-code', but they fail to recognize that these services are optimized for small payloads or real-time streams, not for batch processing of multi-gigabyte files.
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 Azure Data Factory to copy the data from Blob Storage to Azure SQL Database and perform transformations.
Azure Data Factory (ADF) is the correct choice because it is a fully managed, serverless data integration service designed for high-scale data movement and transformation. ADF can directly read large CSV files from Blob Storage using Copy Activity with built-in parallelization and chunking, then transform the data using Mapping Data Flows or stored procedures in Azure SQL Database, all without provisioning VMs or managing infrastructure. This minimizes both processing time (via scale-out) and cost (pay-per-use, no idle compute).
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 Azure Data Factory to copy the data from Blob Storage to Azure SQL Database and perform transformations.
Why this is correct
Azure Data Factory (ADF) is purpose-built for orchestrating and automating large-scale data movement and transformation workflows, making it ideal for batch processing large CSV files. It offers managed integration runtimes, robust connectivity to various data stores like Blob Storage and Azure SQL Database, and powerful data flow capabilities for complex transformations without writing extensive code. ADF's scalable architecture ensures efficient and reliable ingestion and processing of substantial data volumes into a relational database.
- ✗
Use Azure Logic Apps to trigger a function that parses the CSV and inserts into Cosmos DB.
Why it's wrong here
Azure Logic Apps are designed for serverless workflow automation and integration of various services, typically for event-driven, smaller-scale tasks. While a Logic App can trigger an Azure Function to parse a CSV, the inherent limitations on Logic App run duration (up to 365 days, but practical limits for continuous processing are much shorter) and payload size, combined with potential Function App timeouts or memory constraints for very large files, make this approach unsuitable for processing large CSVs efficiently or reliably. It's not optimized for high-throughput batch data processing.
- ✗
Download the file to an Azure VM and use a custom script to parse and insert into a database.
Why it's wrong here
Downloading a large CSV file to an Azure Virtual Machine (VM) and using a custom script introduces significant operational overhead and scalability challenges. This approach requires manual management of the VM, including patching, security, and scaling, which increases administrative burden and costs. Furthermore, a single VM can become a performance bottleneck for very large files, lacks inherent fault tolerance, and is generally less cost-effective and resilient compared to managed, scalable Azure data services designed for such tasks.
- ✗
Use Azure Stream Analytics to read the CSV from Blob Storage and output to a data warehouse.
Why it's wrong here
Azure Stream Analytics is specifically engineered for real-time, continuous processing of unbounded data streams, performing low-latency analytics and aggregations on event data. While it can ingest data from Blob Storage, its core strength lies in processing data as it arrives, not in efficient, one-time batch processing of a large, static CSV file. Using Stream Analytics for a batch scenario would be a misuse of its capabilities, leading to inefficient resource utilization and a mismatch with the problem's batch-oriented requirements.
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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