- A
Implement manual sharding by splitting the large table into multiple smaller tables across instances.
Why wrong: Incorrect: Manual sharding is complex and not recommended; Cloud Spanner handles distribution automatically.
- B
Use interleaved tables to reduce query latency for reads.
Why wrong: Incorrect: Interleaved tables help with read performance, not write capacity.
- C
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Correct: The current node pool's machine type may have insufficient CPU capacity for the write load; a larger machine type provides more vCPUs per node, improving write throughput.
- D
Change the placement configuration to use a dual-region with multiple writable leaders.
Why wrong: Incorrect: Cloud Spanner multi-region configurations support a single writable leader region; dual-region is not available.
Google PCA Manage implementation of cloud architecture 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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Option C is correct because increasing the node count in Cloud Spanner directly increases the total processing capacity (CPU and I/O) available to handle write operations. Since the leader region (us-central1) is at 95% CPU, adding nodes distributes the write load across more split servers, reducing write latency without affecting read performance, as reads can still be served from both regions using the same multi-region configuration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Incorrect: Manual sharding is complex and not recommended; Cloud Spanner handles distribution automatically.
- ✗
Use interleaved tables to reduce query latency for reads.
Why it's wrong here
Incorrect: Interleaved tables help with read performance, not write capacity.
- ✓
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Why this is correct
Correct: The current node pool's machine type may have insufficient CPU capacity for the write load; a larger machine type provides more vCPUs per node, improving write throughput.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the placement configuration to use a dual-region with multiple writable leaders.
Why it's wrong here
Incorrect: Cloud Spanner multi-region configurations support a single writable leader region; dual-region is not available.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing node count only scales storage, but in Cloud Spanner, nodes scale both compute and storage, making it the correct response for CPU-bound write 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
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload. — Option C is correct because increasing the node count in Cloud Spanner directly increases the total processing capacity (CPU and I/O) available to handle write operations. Since the leader region (us-central1) is at 95% CPU, adding nodes distributes the write load across more split servers, reducing write latency without affecting read performance, as reads can still be served from both regions using the same multi-region configuration.
What should I do if I get this PCA question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCA practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCA exam.
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