Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution
You want to create a log-based metric to count errors from your application logs. Which TWO resources are required? (Select 2)
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 filter that matches the error log entries
You need a filter to match error logs and a metric descriptor that defines the metric type.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A filter that matches the error log entries
Why this is correct
In Cloud Logging, a logs-based metric is created by defining a filter that selects which log entries increment the metric's counter. This filter is the heart of the metric because it evaluates each incoming log entry against conditions such as severity >= ERROR or a text payload match. Without it, the metric has no way to distinguish error logs from other entries, so the filter directly determines the metric's value and is therefore required.
- ✗
An alerting policy
Why it's wrong here
An alerting policy is a separate Cloud Monitoring construct that consumes metrics to trigger notifications, but it is not involved in defining or creating the metric itself. You can create a logs-based metric and use it only for dashboards or SLOs without ever attaching an alerting policy. Alerting policies reference an existing metric descriptor and add threshold conditions, so they are downstream consumers, not components of metric creation.
- ✓
A metric descriptor (e.g., name, type, label)
Why this is correct
A metric descriptor is the schema that defines the metric's identity, including its type, name, unit, and label keys. When you create a logs-based metric in Cloud Logging, you must specify a descriptor so that Cloud Monitoring can store and query the resulting count. The descriptor is necessary for the metric to exist as a first-class Monitoring object, but it does not perform the counting — the filter provided alongside it determines which log entries are counted.
- ✗
A log sink
Why it's wrong here
A log sink is a routing configuration that exports raw log entries to destinations such as Cloud Storage, BigQuery, or Pub/Sub. Sinks are designed for log archival and analysis outside Cloud Logging, not for aggregating entries into numeric metrics. Since a logs-based metric is computed internally by Cloud Logging as entries arrive, the sink does not participate in metric creation and would not help count errors.
- ✗
A notification channel
Why it's wrong here
A notification channel is a delivery mechanism for alert notifications, such as email, SMS, Slack, or webhooks. Notification channels are attached to alerting policies to tell users when a condition is breached, but they have no function in the aggregation or counting of log entries. Creating a logs-based metric does not require a notification channel, and configuring one would not affect how the error count is produced.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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