- A
Implement manual sharding by splitting the large table into multiple smaller tables across instances.
Why wrong: Manual sharding adds operational complexity and does not directly address the leader region CPU bottleneck; it may introduce consistency challenges.
- B
Use interleaved tables to reduce query latency for reads.
Why wrong: Interleaved tables improve read performance by physically co-locating related rows, but they do not reduce write load on the leader region.
- C
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Why wrong: Cloud Spanner does not support node pools with different machine types; adding nodes to the instance is the correct scaling method, not creating a node pool with higher vCPUs.
- D
Change the placement configuration to use a dual-region with multiple writable leaders.
Correct. Changing to a dual-region with multiple writable leaders allows both regions to process writes, reducing the load on the original leader and decreasing write latency.
Google PCA Cloud Spanner multi-region Practice Question
This PCA practice question tests your understanding of manage implementation of cloud architecture. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: cloud Spanner multi-region. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A global e-commerce platform uses Cloud Spanner in a multi-region configuration across us-central1 (leader) and europe-west1. The application writes all orders to a single table and reads from both regions. During a flash sale, write latency spikes, causing order failures. The team notices that the leader region's CPU utilization is at 95%, while the europe-west1 region is mostly idle. The application uses partitioned DML for batch updates. The development team proposes increasing node count. What should the architect do to reduce write latency while maintaining global read performance?
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
Change the placement configuration to use a dual-region with multiple writable leaders.
Option D is correct because configuring a dual-region with multiple writable leaders distributes write operations across both regions, reducing CPU utilization on the original leader region. This approach improves write latency during high-traffic events like a flash sale while maintaining global read performance from both regions. Scaling by adding nodes (as proposed) is also valid, but among the given choices, changing the placement configuration directly addresses the single-leader bottleneck.
Key principle: Cloud Spanner multi-region
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement manual sharding by splitting the large table into multiple smaller tables across instances.
Why it's wrong here
Manual sharding adds operational complexity and does not directly address the leader region CPU bottleneck; it may introduce consistency challenges.
- ✗
Use interleaved tables to reduce query latency for reads.
Why it's wrong here
Interleaved tables improve read performance by physically co-locating related rows, but they do not reduce write load on the leader region.
- ✗
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Why it's wrong here
Cloud Spanner does not support node pools with different machine types; adding nodes to the instance is the correct scaling method, not creating a node pool with higher vCPUs.
- ✓
Change the placement configuration to use a dual-region with multiple writable leaders.
Why this is correct
Correct. Changing to a dual-region with multiple writable leaders allows both regions to process writes, reducing the load on the original leader and decreasing write latency.
Related concept
Cloud Spanner multi-region
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that Cloud Spanner only supports a single leader for writes in any configuration. However, using a dual-region with multiple writable leaders allows both regions to handle writes, distributing the load and reducing latency.
Detailed technical explanation
How to think about this question
Cloud Spanner uses a shared-nothing architecture where each node provides a fixed amount of compute and storage capacity (approximately 2 TB of storage and 2,000 QPS of reads or 1,000 QPS of writes per node). When CPU exceeds 80%, write latency increases due to contention on split servers and transaction processing; adding nodes increases the number of splits and reduces per-node load. Partitioned DML operations are particularly CPU-intensive because they execute across multiple splits, so scaling nodes is the recommended mitigation for write-heavy workloads.
KKey Concepts to Remember
- Cloud Spanner multi-region
- Write latency
- Placement configuration
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Cloud Spanner multi-region
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Cloud Spanner multi-region Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this PCA question test?
Manage implementation of cloud architecture — This question tests Manage implementation of cloud architecture — Cloud Spanner multi-region.
What is the correct answer to this question?
The correct answer is: Change the placement configuration to use a dual-region with multiple writable leaders. — Option D is correct because configuring a dual-region with multiple writable leaders distributes write operations across both regions, reducing CPU utilization on the original leader region. This approach improves write latency during high-traffic events like a flash sale while maintaining global read performance from both regions. Scaling by adding nodes (as proposed) is also valid, but among the given choices, changing the placement configuration directly addresses the single-leader bottleneck.
What should I do if I get this PCA question wrong?
Review cloud Spanner multi-region, then practise related PCA questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Cloud Spanner multi-region
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Last reviewed: Jun 30, 2026
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