Google PCA Ensure solution and operations reliability Practice Question
A company uses Cloud Spanner for a global financial application. They experience increased latency and transaction aborts during peak hours. Which measure should they take first to improve reliability?
⚠ Common exam trap
Google Cloud often tests the misconception that scaling nodes (Option A) is the universal fix for performance issues, but the trap here is that Spanner's horizontal scaling does not resolve lock contention—it only increases parallelism, which can worsen contention if transactions are not optimized.
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
✓
Optimize transactions to reduce lock contention.
Transaction aborts and latency in Cloud Spanner are most commonly caused by lock contention during peak hours. By optimizing transactions—such as reducing their scope, using read-only transactions where possible, and avoiding hot-spot writes—you directly address the root cause of contention without incurring additional cost or schema changes. This aligns with Google's best practices for Spanner reliability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of nodes in the Spanner instance.
Why it's wrong here
Adding nodes increases compute capacity for throughput, but aborts stem from lock contention on hot rows, which extra nodes do not resolve. It is tempting because scaling is the reflex response to latency, and would be correct if CPU saturation, not contention, were the bottleneck.
- ✗
Reduce the number of indexes on frequently updated columns.
Why it's wrong here
Reducing indexes on frequently updated columns lowers write amplification, but the stem's peak-hour latency and aborts stem from lock contention and hotspotting across splits, which index reduction does not address. It is tempting because index pruning genuinely helps write-heavy workloads, yet the first measure should target hotspot distribution or interleaved tables.
- ✓
Optimize transactions to reduce lock contention.
Why this is correct
Reducing lock contention directly addresses the transaction aborts and latency spikes. Cloud Spanner aborts transactions when locks conflict, so shortening transactions and ordering reads and writes to minimise overlapping access lowers abort rates and improves throughput during peak load.
- ✗
Use interleaved tables to co-locate related data.
Why it's wrong here
Interleaving physically co-locates child rows with their parent to speed parent-child joins, which does nothing for the contention causing aborts. It is tempting because interleaving reduces latency for hierarchical reads, and would be correct if the workload were dominated by such joins.
Go deeper
Related to this question
Learn chapter
Billing, Budgets, and Cost Management
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
Key term
Cloud Spanner
Cloud Spanner is a fully managed, globally distributed relational database service from Google Cloud that combines the benefits of relational database structure with horizontal scalability and strong consistency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.