Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution
You need to export logs from Cloud Logging to a BigQuery dataset for long-term analysis. What should you create?
⚠ Common exam trap
ACE often tests the confusion between log-based metrics and log sinks; candidates may choose log-based metrics thinking they export logs, but metrics only aggregate data for monitoring, not export raw logs.
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
✓
A log sink with BigQuery as the destination
To export logs from Cloud Logging to BigQuery, you create a log sink with BigQuery as the destination. Log sinks are the mechanism in Google Cloud for routing log entries to supported destinations, including BigQuery, Cloud Storage, and Pub/Sub. This allows for long-term storage and analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An alerting policy with a log-based trigger
Why it's wrong here
An alerting policy with a log-based trigger is designed to notify you when specific conditions are met in incoming log entries; it can create incidents or send notifications via Cloud Monitoring. It does not route or copy log entries to any external destination. Exporting logs requires a log sink, not an alerting or notification configuration.
- ✗
A log-based metric
Why it's wrong here
A log-based metric computes a numeric value such as a counter or distribution from your log entries and stores that metric in Cloud Monitoring, enabling charts and alerts. It does not persist the raw log data itself, nor does it move the content to BigQuery. To get logs into BigQuery, you need a sink that writes matching entries to a BigQuery dataset.
- ✗
An export job in BigQuery
Why it's wrong here
An export job in BigQuery extracts data from BigQuery tables to formats like Parquet, CSV, or Avro, usually to Cloud Storage. It is an outbound operation, not an import mechanism, and BigQuery has no built-in 'export job' that pulls Cloud Logging data. Incoming logs must be delivered through a log router sink that writes directly to a BigQuery destination.
- ✓
A log sink with BigQuery as the destination
Why this is correct
A log sink with BigQuery as the destination is the correct method: Cloud Logging's log router matches your chosen log entries and delivers them to a BigQuery dataset, where each daily collection becomes a table. You configure the destination by providing a dataset name, and the sink automatically handles batching and streaming writes. This is the officially supported, commonly used way to export logs to BigQuery for analytics.
Go deeper
Related to this question
Learn chapter
Google Cloud Platform Overview
Key term
Cloud storage
Cloud storage is a service that lets you save data on remote servers accessed over the internet instead of on your computer's hard drive.
Key term
Pub/Sub
Pub/Sub is a messaging pattern where publishers send messages without knowing who receives them, and subscribers receive only the messages they care about.
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This ACE question is part of Courseiva's 775-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.