Discovery, portfolio assessment and migration waves: SAP-C03 study notes and practice questions
What to know about Discovery, portfolio assessment and migration waves for the SAP-C03 exam, from the Resilience, Migration, and Business Continuity domain (15% of the exam guide), then 1 free question to test it. Part of the SAP-C03 study guide.
SAP-C03 migration-planning questions describe a data center with hundreds or thousands of servers, a deadline (often a lease or hardware support end) and little documentation. They ask what to do first and how to group the work.
The migration strategies (7 Rs)
Strategy
Means
Typical trigger
Retire
Turn it off
Unused or duplicate applications found in discovery
Retain
Leave it for now
Recently upgraded, compliance hold, not ready
Rehost
Lift and shift to EC2
Fast exit from a data center, few changes allowed
Relocate
Move a platform as-is (such as VMware workloads)
Hypervisor-level move without changing VMs
Repurchase
Move to SaaS
Commodity app such as CRM or email
Replatform
Lift, tinker and shift to a managed service
Move a database to RDS or Aurora, an app to containers
Refactor
Re-architect cloud-native
Scaling, agility or cost goals that justify the effort
Pick per application, based on business value, technical fit and deadline. A common plan is to rehost most servers to meet the deadline, then modernize the important ones after. See rehosting and data migration and modernization.
Discovery and assessment
Collect server inventory, utilization (CPU, memory, disk, network), installed software and network connections over a representative period, often several weeks to capture month-end peaks.
Dependency mapping from network connection data shows which servers talk to each other; that is what turns a server list into applications.
AWS Transform is AWS’s agentic AI service for migration and modernization. It provides discovery with agent-based and agentless collectors, dependency mapping, assessment with EC2 sizing recommendations, business case support and wave planning, and it can hand off to AWS Transform MGN for execution.
AWS Migration Hub and AWS Application Discovery Service stopped accepting new customers on November 7, 2025; new projects start in AWS Transform. Existing customers can still finish projects in them.
Use utilization data to rightsize targets rather than copying on-premises sizes, and use the assessment to build the TCO and business case.
Waves
A wave is a group of applications that migrate together. Put tightly coupled applications and their databases in the same wave so chatty traffic doesn’t cross the hybrid link for long.
Start with a pilot wave of low-risk applications to prove the tooling, landing zone, network and runbooks; then grow wave size.
Each wave has a cutover plan, rollback plan, test plan and owners; a migration factory (repeatable process and teams) handles waves in parallel.
Before wave 1, the landing zone (accounts, network, Direct Connect or VPN, identity, logging) must be in place.
How to choose
Scenario wording
Pick
“No documentation of which servers talk to each other”
Agent-based discovery with dependency mapping
“Plan groups and order of moves for 2,000 servers”
AWS Transform wave planning
“Exit data center in 6 months, minimal change”
Rehost most, modernize later
“Application unused for a year”
Retire
“Off-the-shelf HR system”
Repurchase (SaaS)
Common traps
Starting new projects in Migration Hub or Application Discovery Service, which are closed to new customers.
Splitting an application from its database across waves without accounting for latency.
Sizing targets from on-premises specs instead of measured utilization.
Refactoring everything under a tight deadline.
Migrating before the landing zone and connectivity exist.
A utility company plans to move about 600 applications out of its data centers over three years. The portfolio has no complete list of which servers talk to which applications, and some business systems share a single Oracle database.
The migration office wants waves that move each application together with the systems it depends on, so no wave breaks a live process. Each wave must also fit the maintenance windows the operations team already runs.
Which approach meets these requirements?
Choose one answer.
AA. Group the applications by the department that owns them, and move each department in one wave in the order shown on the org chart.
BB. Use AWS Transform to collect server inventory and network dependency data over a full business cycle, then plan waves that move dependent systems, including shared-database ones, together.
CC. Move the largest applications first, because their cost savings are the biggest, and then move the smaller applications in whatever order the application teams request during each maintenance window.
DD. Migrate all 600 applications in a single weekend cutover with a rollback plan, so that the dependency questions do not need answering before the move.
Show the answer and explanation
Answer: B
A. Wrong: Department groupings ignore the network and database links between applications, so a wave can move a system while the systems it calls stay behind. The approach breaks live processes.
B. Right: Correct. Dependency data collected over a full cycle exposes the real links between systems, including shared databases and month-end traffic. Waves built from that map keep dependent systems together and can be scheduled inside the existing maintenance windows.
C. Wrong: Size and savings do not show which systems depend on each other, so the first waves can break the smaller systems that call them. The order also ignores the maintenance windows.
D. Wrong: A single cutover of 600 applications has no realistic rollback window, and the operations team would need to coordinate every dependency at once. The approach fails the wave and window requirements.
Explanation
Discovery over a full business cycle reveals the real dependencies, including shared databases, and waves built from it move dependent systems together inside the existing windows. Org-chart groupings, size-based ordering and a single cutover all ignore dependencies or the operations constraints.
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