- A
Datastream with BigQuery as destination
Why wrong: Datastream replicates data as-is; complex transformations require Dataflow.
- B
Dataflow pipeline using JDBC source and BigQuery sink
Dataflow can read from Cloud SQL via JDBC, perform complex transformations, and write to BigQuery.
- C
BigQuery Data Transfer Service for Cloud SQL
Why wrong: BigQuery Data Transfer Service does not support Cloud SQL as a source.
- D
Cloud Functions triggered by Cloud Scheduler
Why wrong: Cloud Functions have a 9-minute timeout and are not ideal for complex transformations on large data volumes.
Question 25 of 980
PCD Practice Question: Manage a Solution that Can Span Multiple Database Systems
This PCD practice question tests your understanding of manage a solution that can span multiple database 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.
An online gaming company uses Cloud SQL for MySQL for player data and wants to synchronise some of that data to a BigQuery dataset daily for analytics. The data transformations are complex (e.g., aggregations, joins with other sources). Which service should they use to build this pipeline?
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
✓
Dataflow pipeline using JDBC source and BigQuery sink
Option B is correct because Dataflow provides a fully managed, serverless execution environment for Apache Beam pipelines, which can read from Cloud SQL via a JDBC source, perform complex transformations (aggregations, joins with other sources) in a distributed manner, and write the results to BigQuery as a sink. This directly addresses the requirement for complex, daily transformations that are beyond the capabilities of simpler services.
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.
- ✗
Datastream with BigQuery as destination
Why it's wrong here
Datastream replicates data as-is; complex transformations require Dataflow.
- ✓
Dataflow pipeline using JDBC source and BigQuery sink
Why this is correct
Dataflow can read from Cloud SQL via JDBC, perform complex transformations, and write to BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery Data Transfer Service for Cloud SQL
Why it's wrong here
BigQuery Data Transfer Service does not support Cloud SQL as a source.
- ✗
Cloud Functions triggered by Cloud Scheduler
Why it's wrong here
Cloud Functions have a 9-minute timeout and are not ideal for complex transformations on large data volumes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Datastream's CDC replication (which is for continuous, near-real-time sync without transformations) with the need for a batch ETL pipeline that requires complex transformations, leading them to incorrectly choose Option A.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow uses the Apache Beam SDK, which allows you to define a pipeline that reads from Cloud SQL using the JDBCIO connector (which manages connection pooling and parallel reads via a 'read' transform with a 'query' or 'table' parameter), applies transformations using Beam's PTransform objects (e.g., GroupByKey, Combine, CoGroupByKey for joins), and writes to BigQuery using the BigQueryIO connector (which supports batch loads via the Storage Write API or legacy streaming inserts). A real-world scenario is a gaming company that needs to join daily player session logs from Cloud SQL with event data from Pub/Sub and aggregate by region, which Dataflow can handle at scale with exactly-once processing semantics.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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?
Manage a Solution that Can Span Multiple Database Systems — This question tests Manage a Solution that Can Span Multiple Database Systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Dataflow pipeline using JDBC source and BigQuery sink — Option B is correct because Dataflow provides a fully managed, serverless execution environment for Apache Beam pipelines, which can read from Cloud SQL via a JDBC source, perform complex transformations (aggregations, joins with other sources) in a distributed manner, and write the results to BigQuery as a sink. This directly addresses the requirement for complex, daily transformations that are beyond the capabilities of simpler services.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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