AZ-305 Question 205
Select 2You are the lead architect for a company planning to move 50 on-premises Windows and Linux servers to Azure. You need to accurately discover current resource utilization, identify application dependencies, and assess compatibility for a smooth migration. Which two actions should you take to properly evaluate on-premises servers for Azure migration?
- A
Deploy Azure Migrate to discover servers, collect performance metrics, and map application dependencies.
- B
Compile a basic spreadsheet of server names and skip compatibility testing, relying on the default Azure settings.
- C
Use agent-based or agentless data collection in Azure Migrate to analyze real-time network traffic and CPU usage trends.
- D
Rely on random server logs to approximate resource consumption, assuming unused resources can be migrated as is.
Show answer and explanation
Correct answers: A, C
Explanation
Azure Migrate is the recommended toolset for discovering on-premises servers, collecting performance metrics, and identifying dependencies prior to migration. Manual or haphazard approaches (like spreadsheets and random log checks) are error-prone and do not capture the complete picture. For more guidance, refer to the official Azure Migrate documentation at https://learn.microsoft.com/azure/migrate/overview.
- A. Correct.
Deploying Azure Migrate is a best practice. It automatically collects data on server performance, operating systems, software inventory, and mapping dependencies, providing critical insights for planning and compatibility checks.
- B. Incorrect.
Simply creating a basic server list without testing compatibility or gathering detailed usage data is too limited and can lead to inaccurate sizing or missed dependencies.
- C. Correct.
Azure Migrate supports both agent-based and agentless methods to gather deeper insights into server workloads. This data is vital to plan correctly sized Azure resources and understand dependency mapping.
- D. Incorrect.
Relying on random logs is inadequate. Migration plans require systematic collection of performance metrics and dependency information. Inconsistent data can result in incorrect sizing and potential migration failures.