Question 691 of 1,446
Design innovative, scalable, and highly available cloud database solutions →mediumMultiple SelectObjective-mapped
PCDE Practice Question: Design innovative, scalable, and highly available cloud database solutions
A company is designing a Cloud Spanner database for a global inventory system. The application runs OLTP transactions on inventory levels and also needs to generate daily reports that scan the entire inventory table. Which two approaches will reduce the impact of analytical queries on transactional performance?
⚠ Common exam trap
A common mistake is to think that simply scaling up resources (Option D) or using strong consistency (Option B) can solve workload isolation problems, when in reality architectural separation via read-only replicas or data export is required to prevent analytical queries from starving transactional operations.
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
✓
Export data to BigQuery daily for reporting
Exporting data to BigQuery offloads analytical workloads from Cloud Spanner entirely, preventing large scans from competing for Spanner's CPU and memory resources. BigQuery is purpose-built for analytical queries on large datasets, so daily exports ensure transactional performance remains unaffected by reporting queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Export data to BigQuery daily for reporting
Why this is correct
Offloads analytical queries entirely from Spanner, preventing any impact on OLTP.
- ✗
Use strong reads for all queries to ensure consistency
Why it's wrong here
Strong reads can increase contention; stale reads are better for analytics to reduce impact.
- ✓
Use read-only replicas in separate regions for analytical queries
Why this is correct
Read-only replicas handle read traffic without impacting primary instance writes.
- ✗
Increase the number of processing units to handle both workloads
Why it's wrong here
Scaling up may help but is not a targeted approach; analytical queries still compete with transactions.
- ✗
Use interleaved tables for inventory items
Why it's wrong here
Interleaving improves join performance but does not reduce impact of large scans on transactional workload.
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Last reviewed: Jul 4, 2026
This PCDE 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 PCDE exam.
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