- A
Use Spanner's read-only transactions to run analytic queries.
Why wrong: Read-only transactions still consume I/O and CPU on Spanner nodes, which can degrade transaction processing performance.
- B
Enable Cloud Spanner's query optimizer for analytical workloads.
Why wrong: Spanner's query optimizer is always active; there is no separate mode for analytical workloads.
- C
Export data from Spanner to BigQuery periodically and run analytic queries there.
This offloads analytical workloads to BigQuery, which is optimized for large-scale analytics, and does not affect Spanner's transactional performance.
- D
Create secondary indexes on the Spanner tables to speed up analytical queries.
Why wrong: Secondary indexes can help but still execute on the same Spanner nodes, potentially impacting transactional throughput.
Quick Answer
The correct approach is to export data from Spanner to BigQuery periodically and run analytic queries there. This works because BigQuery is a serverless, highly scalable data warehouse built for complex analytical queries on massive datasets, while Spanner is optimized for high-throughput, low-latency OLTP transactions. By offloading Spanner analytical queries to BigQuery, you isolate heavy aggregations from the transactional path, preventing resource contention and ensuring OLTP performance remains unaffected. On the Google Professional Cloud Developer exam, this scenario tests your understanding of workload separation between transactional and analytical systems; a common trap is assuming Spanner’s built-in features like interleaved tables or secondary indexes can handle large-scale analytics, but they are designed for point lookups and small joins, not full table scans. Remember the memory tip: “Spanner for transactions, BigQuery for analytics—keep them separate to keep them fast.”
PCD Practice Question: Designing highly scalable, available, and reliable cloud-native applications
This PCD practice question tests your understanding of designing highly scalable, available, and reliable cloud-native applications. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 financial services company uses Cloud Spanner for transactional data. They need to perform complex analytical queries that aggregate large volumes of data without affecting the performance of transaction processing. Which approach should they take?
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 from Spanner to BigQuery periodically and run analytic queries there.
Option C is correct because BigQuery is a serverless, highly scalable data warehouse designed for complex analytical queries on large datasets. By exporting Spanner transactional data to BigQuery, the company can run heavy aggregations without impacting Spanner's OLTP performance, as Spanner is optimized for high-throughput, low-latency transactions, not analytical workloads.
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.
- ✗
Use Spanner's read-only transactions to run analytic queries.
Why it's wrong here
Read-only transactions still consume I/O and CPU on Spanner nodes, which can degrade transaction processing performance.
- ✗
Enable Cloud Spanner's query optimizer for analytical workloads.
Why it's wrong here
Spanner's query optimizer is always active; there is no separate mode for analytical workloads.
- ✓
Export data from Spanner to BigQuery periodically and run analytic queries there.
Why this is correct
This offloads analytical workloads to BigQuery, which is optimized for large-scale analytics, and does not affect Spanner's transactional performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create secondary indexes on the Spanner tables to speed up analytical queries.
Why it's wrong here
Secondary indexes can help but still execute on the same Spanner nodes, potentially impacting transactional throughput.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that Spanner's read-only transactions or indexing can handle analytical workloads without performance degradation, but the key trap is that Spanner is an OLTP database, not an OLAP system, and mixing workloads violates the principle of separating concerns for scalability and reliability.
Detailed technical explanation
How to think about this question
Under the hood, Spanner uses TrueTime and Paxos for distributed transactions, which prioritize consistency and isolation (serializable isolation). Running heavy analytical queries (e.g., full table scans with aggregations) on Spanner can lead to increased lock contention and CPU saturation, degrading transaction latency. Exporting data to BigQuery leverages columnar storage and distributed query execution (using Dremel technology), enabling sub-second query times on terabytes of data without affecting the source transactional system.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCD question test?
Designing highly scalable, available, and reliable cloud-native applications — This question tests Designing highly scalable, available, and reliable cloud-native applications — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export data from Spanner to BigQuery periodically and run analytic queries there. — Option C is correct because BigQuery is a serverless, highly scalable data warehouse designed for complex analytical queries on large datasets. By exporting Spanner transactional data to BigQuery, the company can run heavy aggregations without impacting Spanner's OLTP performance, as Spanner is optimized for high-throughput, low-latency transactions, not analytical workloads.
What should I do if I get this PCD 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 25, 2026
This PCD 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 PCD exam.
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