PAS-C01 Migration Practice Question
A company is migrating its SAP Business Warehouse (BW) system from on-premises to AWS. The system uses IBM DB2 as the database and is 2 TB in size. The company wants to use the SAP BW/4HANA version on AWS. The migration must be completed within a 72-hour window. The company has a 1 Gbps AWS Direct Connect connection. The migration team decides to use the SAP Software Update Manager (SUM) with the Database Migration Option (DMO) to migrate and upgrade to BW/4HANA. During the migration, they encounter an error that the source DB2 database has a tablespace that uses automatic storage and is larger than 2 TB. The SUM DMO tool stops with an error indicating insufficient space in the target SAP HANA database. The target SAP HANA system is provisioned with 1 TB of memory and 2 TB of disk storage. The team needs to proceed with the migration. Which action should the team take to resolve this issue?
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
✓
Increase the disk storage of the SAP HANA instance to at least 2.5 TB to accommodate the migrated data.
The error occurs because the source DB2 database's tablespace using automatic storage exceeds the target SAP HANA's disk capacity of 2 TB. The SUM DMO tool requires enough space to store the migrated data during the conversion process. Increasing the disk storage of the SAP HANA instance to at least 2.5 TB (or larger to accommodate the full 2 TB source plus overhead) resolves the space issue and allows the migration to proceed. Option A is incorrect because AWS DMS does not support SAP HANA as a target for direct migration. Option C is incorrect because DB2 compression reduces storage but does not address the automatic storage tablespace size limitation that causes the SUM error. Option D is incorrect because splitting the migration into DMS and SUM phases is not supported for DB2 to HANA; SUM DMO is the recommended tool for this combined migration and upgrade.
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 AWS Database Migration Service (AWS DMS) to migrate the data directly to SAP HANA, bypassing SUM DMO.
Why it's wrong here
AWS DMS is unsuitable here because it performs raw database migration and replication, not the SAP application-level upgrade and data model transformation required for a BW/4HANA conversion. The SUM DMO tool is specifically chosen for its capability to combine the database migration with the SAP system upgrade. AWS DMS is tempting as a general heterogeneous database migration service, and it would be appropriate for migrating a standalone DB2 database to SAP HANA without an accompanying SAP application upgrade or conversion. The current issue is with space *during* the DMO process, not the choice of DMO itself.
- ✓
Increase the disk storage of the SAP HANA instance to at least 2.5 TB to accommodate the migrated data.
Why this is correct
Increasing disk storage provides the necessary space for the migration.
- ✗
Compress the DB2 database on the source side to reduce its size before migration.
Why it's wrong here
Compression may not reduce size enough, and the issue is the tablespace size being larger than disk.
- ✗
Split the migration into two phases: migrate the data using AWS DMS first, then use SUM for the upgrade.
Why it's wrong here
AWS DMS does not support SAP HANA as a target, and splitting is not a standard approach.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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