- A
Use Datastream to stream Bigtable changes to BigQuery
Why wrong: Datastream supports CDC for relational databases, not for Bigtable.
- B
Set up a Dataflow pipeline on a schedule that reads from Bigtable, transforms, and writes to BigQuery
Dataflow can handle large volumes, complex transformations, and schedule-based execution.
- C
Use BigQuery federated queries to query Bigtable directly for hourly reports
Why wrong: Federated queries do not persist data in BigQuery and do not support transformations; they also have performance limitations.
- D
Write a Cloud Function that queries Bigtable and loads data into BigQuery every hour
Why wrong: Cloud Functions have a 9-minute timeout limit, which may be insufficient for large volumes; also, complex transformations are harder to implement.
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.
A financial services company uses a polyglot persistence architecture: Cloud SQL for MySQL for transactions, Cloud Bigtable for real-time risk calculations, and BigQuery for historical analytics. They need to move data from Bigtable to BigQuery every hour for reporting, with transformations. Which approach is MOST cost-effective and maintainable?
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
Set up a Dataflow pipeline on a schedule that reads from Bigtable, transforms, and writes to BigQuery
Option B is correct because Dataflow provides a fully managed, serverless execution environment that can read from Bigtable, apply transformations (e.g., using Apache Beam's PTransform), and write to BigQuery on a scheduled basis. This approach is cost-effective as it scales to zero when idle and uses autoscaling, and maintainable because the pipeline code is reusable and can be version-controlled. Alternatives either lack transformation capability, incur higher latency, or require custom orchestration that increases operational overhead.
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 Datastream to stream Bigtable changes to BigQuery
Why it's wrong here
Datastream supports CDC for relational databases, not for Bigtable.
- ✓
Set up a Dataflow pipeline on a schedule that reads from Bigtable, transforms, and writes to BigQuery
Why this is correct
Dataflow can handle large volumes, complex transformations, and schedule-based execution.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use BigQuery federated queries to query Bigtable directly for hourly reports
Why it's wrong here
Federated queries do not persist data in BigQuery and do not support transformations; they also have performance limitations.
- ✗
Write a Cloud Function that queries Bigtable and loads data into BigQuery every hour
Why it's wrong here
Cloud Functions have a 9-minute timeout limit, which may be insufficient for large volumes; also, complex transformations are harder to implement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that Datastream can stream from any database, but in reality it only supports specific sources (MySQL, PostgreSQL, Oracle) and cannot read from Bigtable or other NoSQL stores.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow uses the Bigtable connector (based on HBase API) to read rows in parallel via key ranges, and the BigQuery sink uses streaming inserts or batch loads via the Storage Write API. A real-world scenario: if the Bigtable schema uses row keys with timestamps, Dataflow can apply windowing and aggregation (e.g., sliding windows) to produce hourly summaries, which is not possible with federated queries. The pipeline can also handle schema evolution by using DynamicDestinations to write to different BigQuery tables based on row content.
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 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.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Set up a Dataflow pipeline on a schedule that reads from Bigtable, transforms, and writes to BigQuery — Option B is correct because Dataflow provides a fully managed, serverless execution environment that can read from Bigtable, apply transformations (e.g., using Apache Beam's PTransform), and write to BigQuery on a scheduled basis. This approach is cost-effective as it scales to zero when idle and uses autoscaling, and maintainable because the pipeline code is reusable and can be version-controlled. Alternatives either lack transformation capability, incur higher latency, or require custom orchestration that increases operational overhead.
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
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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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