The correct approach is to create a logs sink in each project that exports logs to a BigQuery dataset in the central project. This works because Google Cloud’s logs sink feature uses inclusion filters to route log entries from multiple source projects to a single destination, such as a BigQuery dataset, without requiring any agents or manual data movement. On the Google Professional Cloud Architect exam, this scenario tests your understanding of centralized logging architecture and the distinction between sinks, which aggregate logs across projects, and other methods like exporting to Pub/Sub or using the Logging API directly. A common trap is confusing sinks with the Logs Explorer, which only queries logs within a single project. Remember the memory tip: “Sink it to BigQuery to sync it across the fleet”—each source project needs its own sink, but all point to one central dataset.
Google PCA Manage and provision cloud infrastructure Practice Question
This PCA practice question tests your understanding of manage and provision cloud infrastructure. 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 organization has multiple projects in Google Cloud and wants to centralize logging and monitoring for all projects. They need to aggregate logs from all projects into a single project for analysis. Which approach should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a logs sink in each project that exports logs to a BigQuery dataset in the central project.
Option D is correct because Google Cloud's logs sink feature allows you to route logs from multiple source projects to a centralized BigQuery dataset in a single destination project. This approach aggregates logs efficiently without requiring agents or manual import steps, and it supports real-time log export for analysis.
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.
✗
Export logs from each project to a Cloud Storage bucket and then import them into BigQuery.
Why it's wrong here
Not optimal; sinks directly to BigQuery is better.
✗
Enable Cloud Audit Logs for all projects and view them from the central project.
Why it's wrong here
Audit logs are per project.
✗
Install the Stackdriver agent on all VMs and point them to the central project.
Why it's wrong here
Agents only collect VM logs, not all project logs.
✓
Create a logs sink in each project that exports logs to a BigQuery dataset in the central project.
Why this is correct
Logs sinks can route any log entries to BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the Stackdriver agent (which collects logs from VMs) with the logs sink feature (which routes logs from projects), leading them to choose Option C instead of the correct centralized export method.
Detailed technical explanation
How to think about this question
Logs sinks use a filter-based routing mechanism that can export logs to BigQuery, Cloud Storage, Pub/Sub, or another project's Cloud Logging bucket. Under the hood, each sink creates a log entry export pipeline that respects IAM permissions and can include all logs or specific log types (e.g., compute.googleapis.com). In real-world scenarios, this is critical for compliance audits where logs from hundreds of projects must be centralized in a single BigQuery dataset for SQL-based analysis.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this PCA question in full detail.
Manage and provision cloud infrastructure — This question tests Manage and provision cloud infrastructure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a logs sink in each project that exports logs to a BigQuery dataset in the central project. — Option D is correct because Google Cloud's logs sink feature allows you to route logs from multiple source projects to a centralized BigQuery dataset in a single destination project. This approach aggregates logs efficiently without requiring agents or manual import steps, and it supports real-time log export for analysis.
What should I do if I get this PCA 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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Question Discussion
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