Question 426 of 953

Configure and manage automation of tasks →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct choice is to develop an Azure SQL Database project in Visual Studio, generate a DACPAC, and use Elastic Database Jobs with a custom tracking table to apply the DACPAC to each tenant database in batches, integrating with Azure DevOps to trigger the job after build. This solution works because Elastic Database Jobs are purpose-built for executing T-SQL scripts or deploying DACPACs across a target group of Azure SQL databases, and a custom tracking table allows you to monitor which tenants have been updated and supports rollback for individual databases by re-running a previous DACPAC. On the DP-300 exam, this scenario tests your understanding of multi-tenant schema management and the limitations of Azure SQL Database—specifically that SQL Agent is unavailable, making Elastic Database Jobs the only native orchestration tool for rolling deployments. A common trap is choosing Azure Data Factory or Automation, but remember: Data Factory moves data, not schema, and Automation lacks database-level tracking. Memory tip: “DACPAC + Jobs = rolling tenant updates with rollback.”

DP-300 Configure and manage automation of tasks Practice Question

This DP-300 practice question tests your understanding of configure and manage automation of tasks. 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.

You are a database administrator for a SaaS company that uses Azure SQL Database with elastic pools. The company has hundreds of databases (one per tenant). You need to automate the deployment of schema changes (e.g., adding new columns, creating indexes) across all tenant databases. The changes must be deployed in a rolling fashion to avoid affecting all tenants at once. The automation must track which databases have been updated and allow for rollback of individual tenant databases if needed. Additionally, the solution must integrate with Azure DevOps CI/CD pipelines. What should you do?

Question 1hardmultiple choice

Full question →

A
Use Azure Data Factory with a ForEach activity to execute stored procedures in each database.
Why wrong: Data Factory is not designed for schema deployment and lacks version control.
B
Create an Azure Automation runbook that connects to each database sequentially and runs ALTER TABLE statements.
Why wrong: Does not provide tracking or rollback capabilities easily.
C
Configure SQL Agent jobs on each database to run the schema changes.
Why wrong: SQL Agent is not available in Azure SQL Database.
D
Develop an Azure SQL Database project in Visual Studio, generate a DACPAC, and use Elastic Database Jobs with a custom tracking table to apply the DACPAC to each tenant database in batches. Integrate with Azure DevOps to trigger the job after build.
DACPAC provides version-controlled schema deployment, and Elastic Jobs allows rolling out to specific databases with tracking.

Full breakdown with real-world context →

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

✓

Develop an Azure SQL Database project in Visual Studio, generate a DACPAC, and use Elastic Database Jobs with a custom tracking table to apply the DACPAC to each tenant database in batches. Integrate with Azure DevOps to trigger the job after build.

Option D is correct. Using a combination of Azure SQL Database project (DACPAC) for schema definition and Elastic Database Jobs for targeted deployment allows rolling updates per tenant. Azure DevOps can trigger the jobs. Option A is incorrect because Azure Automation is not designed for multi-tenant schema deployment. Option B is incorrect because Data Factory is for data movement, not schema deployment. Option C is incorrect because SQL Agent is not available in Azure SQL Database.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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 with a ForEach activity to execute stored procedures in each database.
Why it's wrong here
Data Factory is not designed for schema deployment and lacks version control.
✗
Create an Azure Automation runbook that connects to each database sequentially and runs ALTER TABLE statements.
Why it's wrong here
Does not provide tracking or rollback capabilities easily.
✗
Configure SQL Agent jobs on each database to run the schema changes.
Why it's wrong here
SQL Agent is not available in Azure SQL Database.
✓
Develop an Azure SQL Database project in Visual Studio, generate a DACPAC, and use Elastic Database Jobs with a custom tracking table to apply the DACPAC to each tenant database in batches. Integrate with Azure DevOps to trigger the job after build.
Why this is correct
DACPAC provides version-controlled schema deployment, and Elastic Jobs allows rolling out to specific databases with tracking.
Related concept
Static NAT maps one inside address to one outside address.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-300 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this DP-300 question test?

Configure and manage automation of tasks — This question tests Configure and manage automation of tasks — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Develop an Azure SQL Database project in Visual Studio, generate a DACPAC, and use Elastic Database Jobs with a custom tracking table to apply the DACPAC to each tenant database in batches. Integrate with Azure DevOps to trigger the job after build. — Option D is correct. Using a combination of Azure SQL Database project (DACPAC) for schema definition and Elastic Database Jobs for targeted deployment allows rolling updates per tenant. Azure DevOps can trigger the jobs. Option A is incorrect because Azure Automation is not designed for multi-tenant schema deployment. Option B is incorrect because Data Factory is for data movement, not schema deployment. Option C is incorrect because SQL Agent is not available in Azure SQL Database.

What should I do if I get this DP-300 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

About these practice questions

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Same concept, more angles

1 more ways this is tested on DP-300

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. You need to automate the execution of a T-SQL script against all user databases in an Azure SQL Database elastic pool. The script should run on a schedule and results should be logged to a table. Which feature should you use?

medium

A.SQL Agent jobs
B.Azure Data Factory
C.Azure Automation with PowerShell runbooks
✓ D.Elastic Database Jobs

Why D: Elastic Database Jobs are designed for executing T-SQL across many databases in a pool. SQL Agent is not available in Azure SQL Database. Azure Automation runbooks can run T-SQL but require more setup. Azure Data Factory is for data movement, not T-SQL execution.

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