Quick Answer
The answer is Option D, using an Azure SQL Database elastic job to run a T-SQL script for low-cost ETL within the same database. This is correct because the elastic job executes T-SQL directly on the Azure SQL Database, eliminating data movement and external compute costs, while performing aggregations and joins natively—perfect for a 10 GB workload with basic transformations. On the DP-203 exam, this scenario tests your ability to match the simplest, most cost-effective tool to a small business with limited budget and no dedicated data engineering team; a common trap is overcomplicating with Azure Data Factory or Databricks when the data never leaves the database. The key insight is that elastic jobs are essentially scheduled T-SQL scripts, ideal for in-database ETL that minimizes administrative effort. Memory tip: “Keep it in the DB” — if your source and target are the same Azure SQL Database and transformations are T-SQL, elastic jobs are your cheapest, simplest bet.
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A small business uses Azure SQL Database as their operational data store. They want to build a simple reporting solution that refreshes daily. The data volume is about 10 GB. They have limited budget and no dedicated data engineering team. They need to extract data from Azure SQL Database, perform basic transformations (e.g., aggregations, joins), and load into a reporting table in the same database. They want to minimize administrative effort and cost. Which approach should they take?
Option A: Use Azure Data Factory with a copy activity to copy data from the source to a staging table, then use a stored procedure activity to run T-SQL transformations.
Option B: Use Azure Logic Apps to periodically query the source and insert transformed data into the reporting table.
Option C: Use Azure Databricks notebooks scheduled via Azure Data Factory to perform the ETL.
Option D: Use Azure SQL Database elastic job to run a T-SQL script that performs the ETL within the database.
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Option B
Why wrong: Not suitable for complex transformations.
Option A
Why wrong: Adds unnecessary cost and complexity.
Option D
Simple, cost-effective, and fits requirements.
Option C
Why wrong: Too heavy for small scale.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Option D
Option D is correct because using an Azure SQL Database elastic job to run a T-SQL script directly within the database is the simplest, lowest-cost, and lowest-administration approach for this scenario. It avoids external services, requires no data movement, and leverages existing SQL skills, making it ideal for a small business with limited budget and no dedicated data engineering team.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Option B
Why it's wrong here
Not suitable for complex transformations.
Option A
Why it's wrong here
Adds unnecessary cost and complexity.
Option D
Why this is correct
Simple, cost-effective, and fits requirements.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Option C
Why it's wrong here
Too heavy for small scale.
Common exam traps
The trap here is that candidates often over-engineer the solution by choosing Azure Data Factory or Databricks, assuming that any ETL requires a dedicated orchestration or big-data tool, when in fact a simple in-database job is the most cost-effective and administratively lightweight option for small-scale, in-place transformations.
Detailed technical explanation
Elastic jobs in Azure SQL Database allow you to schedule T-SQL scripts that run directly against the database, leveraging the same engine for both extraction and transformation. This approach avoids data egress costs and network latency, as all operations happen in-place. In a real-world scenario, a small business could use a single job with a T-SQL script that performs SELECT INTO or INSERT INTO with aggregations and joins, scheduled to run daily, with built-in retry and logging via the job system.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Option D — Option D is correct because using an Azure SQL Database elastic job to run a T-SQL script directly within the database is the simplest, lowest-cost, and lowest-administration approach for this scenario. It avoids external services, requires no data movement, and leverages existing SQL skills, making it ideal for a small business with limited budget and no dedicated data engineering team.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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