PAS-C01 Design of SAP Workloads on AWS Practice Question
An SAP administrator notices that the SAP HANA database is running out of memory frequently. The system is deployed on an EC2 instance with 512 GB of RAM. The HANA memory usage is peaking at 480 GB. What should the administrator do to resolve this issue?
⚠ Common exam trap
The trap is that candidates might think enabling memory overcommit (Option B) is sufficient, but they overlook that the current peak usage exceeds the 90% limit of the existing instance. The correct approach is to upgrade to a larger instance to provide adequate memory headroom.
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
✓
Migrate to an instance with more memory, such as x1e.32xlarge
The HANA database is peaking at 480 GB on a 512 GB instance, indicating that memory is nearly fully utilized. Enabling memory overcommit and setting the HANA memory limit to 90% (460.8 GB) would still be below the peak usage, potentially causing out-of-memory errors. The most straightforward and reliable solution is to migrate to a larger instance type, such as x1e.32xlarge, which offers 976 GB of RAM, providing sufficient headroom for current and future peak demands. This aligns with AWS best practices for SAP HANA workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a swap file on the instance's root EBS volume
Why it's wrong here
Incorrect. Configuring a swap file on the root EBS volume is not recommended for SAP HANA because it introduces significant latency and can degrade database performance. HANA is designed to run in-memory and swap should be avoided.
- ✗
Enable HANA memory overcommit and set the memory limit to 90%
Why it's wrong here
Incorrect. While enabling memory overcommit and setting a memory limit is a common practice, with current peak usage at 480 GB, a 90% limit (460.8 GB) is insufficient. This could lead to OOM kills or HANA crashes. The immediate fix is to increase available memory.
- ✓
Migrate to an instance with more memory, such as x1e.32xlarge
Why this is correct
Correct. Migrating to a larger instance like x1e.32xlarge provides 976 GB of RAM, which comfortably accommodates the current peak of 480 GB and allows for growth. This is the most effective solution to resolve the out-of-memory issue.
- ✗
Reduce the number of HANA table partitions
Why it's wrong here
Incorrect. Reducing the number of HANA table partitions might improve performance but does not directly address the memory shortage. Table partitioning affects storage and query optimization, not memory consumption at the system level.
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