- A
Schedule a daily Cloud Function to query the Logging API and write results to BigQuery
Why wrong: A daily batch export has significant latency and gaps — log sinks provide near-real-time continuous export automatically.
- B
Create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset
Log sinks continuously route matching log entries to BigQuery in near-real-time. Once configured, future entries are exported automatically without manual steps.
- C
Use BigQuery Data Transfer Service to pull logs from Cloud Logging on a schedule
Why wrong: BigQuery Data Transfer Service transfers data from external sources like Google Ads — it doesn't natively pull Cloud Logging data.
- D
Enable VPC Flow Logs and stream them directly to BigQuery
Why wrong: VPC Flow Logs capture network traffic metadata, not all Cloud Logging entries. They also require a log sink for BigQuery export.
Quick Answer
The correct answer is to create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset. This works because a log sink is a filter-driven export mechanism within Cloud Logging that streams log entries in near-real-time to a destination like BigQuery, automatically capturing all future entries without any batch jobs or manual scheduling. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of the Log Router’s ability to handle continuous, compliance-driven exports, often appearing as a distractor against cron jobs or Pub/Sub-based pipelines. A common trap is assuming you need a scheduled query or a Dataflow job, but the Log Router’s sink is purpose-built for this exact use case. Remember the memory tip: “Sink it and forget it”—once the sink is configured, logs flow automatically, making it the simplest and most reliable near-real-time solution for long-term analysis.
Google ACE Practice Question: Ensuring successful operation of a cloud solution
This ACE practice question tests your understanding of ensuring successful operation of a cloud solution. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 team needs to export all Cloud Logging entries from a production GCP project to a BigQuery dataset for long-term analysis and compliance. The export must be near-real-time and include future log entries automatically. Which approach achieves this?
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
Create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset
Option B is correct because a log sink (Log Router) in Cloud Logging can be configured to route log entries in near-real-time to a BigQuery dataset. This approach automatically includes all future log entries without requiring any scheduled jobs or manual intervention, making it ideal for long-term analysis and compliance.
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.
- ✗
Schedule a daily Cloud Function to query the Logging API and write results to BigQuery
Why it's wrong here
A daily batch export has significant latency and gaps — log sinks provide near-real-time continuous export automatically.
- ✓
Create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset
Why this is correct
Log sinks continuously route matching log entries to BigQuery in near-real-time. Once configured, future entries are exported automatically without manual steps.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use BigQuery Data Transfer Service to pull logs from Cloud Logging on a schedule
Why it's wrong here
BigQuery Data Transfer Service transfers data from external sources like Google Ads — it doesn't natively pull Cloud Logging data.
- ✗
Enable VPC Flow Logs and stream them directly to BigQuery
Why it's wrong here
VPC Flow Logs capture network traffic metadata, not all Cloud Logging entries. They also require a log sink for BigQuery export.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse BigQuery Data Transfer Service with a general-purpose data ingestion tool, but it does not support Cloud Logging as a source, leading them to choose option C instead of the correct log sink approach.
Detailed technical explanation
How to think about this question
A log sink uses the Log Router to filter and route log entries based on inclusion/exclusion filters, and it can export to BigQuery via a dataset in the same or different project. The export is near-real-time because the Log Router streams entries as they are ingested, with typical latency under a minute. For compliance, sinks support features like log exclusions to avoid duplicating sensitive data and can be configured with a unique writer identity for fine-grained access control.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this ACE question test?
Ensuring successful operation of a cloud solution — This question tests Ensuring successful operation of a cloud solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a log sink (Log Router) that routes all logs from the project to the BigQuery dataset — Option B is correct because a log sink (Log Router) in Cloud Logging can be configured to route log entries in near-real-time to a BigQuery dataset. This approach automatically includes all future log entries without requiring any scheduled jobs or manual intervention, making it ideal for long-term analysis and compliance.
What should I do if I get this ACE 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 11, 2026
This ACE 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 ACE exam.
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