This PDE practice question tests your understanding of designing data processing systems. 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 company needs to process real-time clickstream data and store it in a data warehouse for SQL-based analytics. The data volume is moderate. Which combination of Google Cloud services is most cost-effective?
Cloud Pub/Sub, Cloud Dataproc, Cloud Storage
Why wrong: Dataproc overhead.
Cloud Pub/Sub, Cloud Dataflow, Cloud Spanner
Why wrong: Spanner expensive for analytics.
Cloud Pub/Sub, Cloud Dataflow, BigQuery
Best for real-time SQL analytics.
Cloud Pub/Sub, Cloud Dataflow, Cloud Storage
Why wrong: Cloud Storage not SQL-queryable.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Pub/Sub, Cloud Dataflow, BigQuery
Option C is correct because Cloud Pub/Sub ingests real-time clickstream data, Cloud Dataflow processes it with low latency, and BigQuery provides a serverless, SQL-based data warehouse that is cost-effective for moderate data volumes due to its pay-per-query pricing and automatic scaling. This combination avoids the overhead of managing clusters (Dataproc) or expensive storage (Cloud Spanner) while directly supporting SQL analytics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Pub/Sub, Cloud Dataproc, Cloud Storage
Why it's wrong here
Dataproc overhead.
Cloud Pub/Sub, Cloud Dataflow, Cloud Spanner
Why it's wrong here
Spanner expensive for analytics.
Cloud Pub/Sub, Cloud Dataflow, BigQuery
Why this is correct
Best for real-time SQL analytics.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Pub/Sub, Cloud Dataflow, Cloud Storage
Why it's wrong here
Cloud Storage not SQL-queryable.
Common exam traps
Google Cloud often tests the misconception that Cloud Storage is a suitable destination for analytics-ready data, but it lacks native SQL querying, forcing candidates to overlook BigQuery's direct integration with Dataflow for real-time analytics.
Detailed technical explanation
Cloud Dataflow uses the Apache Beam SDK to unify batch and streaming pipelines, automatically handling watermarking and late data for real-time clickstream processing. BigQuery's columnar storage and capacity-based pricing (slots) or on-demand query pricing make it ideal for moderate volumes, as it decouples storage and compute, allowing you to pay only for queries run. In contrast, Cloud Spanner's synchronous replication and Paxos-based consensus add latency and cost that are unnecessary for analytics workloads.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Pub/Sub, Cloud Dataflow, BigQuery — Option C is correct because Cloud Pub/Sub ingests real-time clickstream data, Cloud Dataflow processes it with low latency, and BigQuery provides a serverless, SQL-based data warehouse that is cost-effective for moderate data volumes due to its pay-per-query pricing and automatic scaling. This combination avoids the overhead of managing clusters (Dataproc) or expensive storage (Cloud Spanner) while directly supporting SQL analytics.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PDE 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 PDE exam.
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