PAS-C01 Design of SAP Workloads on AWS Practice Question
A company is running SAP HANA on AWS using a scale-out architecture with multiple worker nodes. The system is used for real-time analytics. Recently, query performance has degraded. The HANA administrator notices that the data is not evenly distributed across nodes. What is the best course of action?
⚠ Common exam trap
It's easy for candidates to confuse scaling up (increasing resources) or scaling out (adding nodes) with the need to rebalance existing data, assuming that more capacity alone will fix performance degradation caused by data skew.
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
✓
Redistribute the tables across nodes using HANA's table partitioning features.
SAP HANA's table partitioning features allow you to redistribute data across worker nodes in a scale-out architecture, addressing the root cause of uneven data distribution. This rebalancing improves query performance by ensuring parallel processing and minimizing data movement between nodes. The HANA administrator can use commands like ALTER TABLE ... REBALANCE or partition management to achieve even distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Redistribute the tables across nodes using HANA's table partitioning features.
Why this is correct
Partitioning and redistribution ensures even data distribution.
- ✗
Increase the memory allocation for the HANA database.
Why it's wrong here
Memory allocation does not affect data distribution.
- ✗
Add more worker nodes to the scale-out cluster to distribute the load.
Why it's wrong here
Adding nodes does not automatically rebalance existing data.
- ✗
Upgrade the EC2 instance type of all nodes to a memory-optimized type.
Why it's wrong here
Memory increase does not solve distribution problem.
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