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Professional Cloud DevOps Engineer
Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations)
An enterprise runs microservices on Google Kubernetes Engine…

Professional Cloud DevOps Engineer

Professional Cloud DevOps Engineer

Advanced

Prepare and test your skills

Description
Sample Question
Free Trial
Practice Test

Advanced

Prepare and test your skills

Description
Sample Question
Free Trial
Practice Test

Optimize Log Retention Tiering Bigquery Linked Datasets

Section: Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations)

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud DevOps Engineer

An enterprise organization is experiencing surging Cloud Logging ingestion costs across multiple production Google Cloud projects. An analysis of log volume via Cloud Monitoring Metrics Explorer reveals two major contributors to the high ingestion bill: GKE system overhead: High-frequency container and pod logs originating from infrastructure namespaces such as kube-system and monitoring-system. Dataflow worker logs: Verbose worker step telemetry from batch pipelines where engineers only require pipeline lifecycle, error, and supportability data to troubleshoot job failures. You must implement a logging optimization strategy that eliminates redundant log ingestion and minimizes storage costs while preserving critical GKE workload logs, Kubernetes audit trails, and essential Dataflow troubleshooting capabilities. What should you do to achieve this objective?

Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations)

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An enterprise web service running on a fleet of Compute Engine Linux VMs generates over 40,000 HTTP access log entries per second per instance during peak traffic. The operations team identifies two major issues: The legacy Cloud Logging agent (google-fluentd) is reaching its single-core CPU limit and dropping log entries due to buffer overflow. Cloud Logging ingestion and storage costs are exceeding budget due to high volumes of redundant HTTP 200 OK access logs. You must design an observability architecture that achieves the following requirements: Ingest 100% of HTTP 5xx error logs for security and root cause analysis. Retain full statistical visibility into total request counts and traffic volume trends across all response codes. Uniformly sample and ingest only 10% of successful HTTP 2xx/3xx access logs into the log bucket. Prevent log dropping and CPU starvation on the VM instances. Which solution should you implement?

Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations)

AdvancedProfessional Cloud DevOps Engineer

Your team is standardizing observability across multiple Google Cloud environments by managing Cloud Monitoring dashboards as code. You need to construct a dashboard chart widget using Prometheus Query Language (PromQL) to visualize the fraction of regional Pub/Sub publisher quota consumed across all active regions. The visualization must meet the following technical requirements: Group metrics by both quota_metric and location. Dynamically adapt calculation windows based on the dashboard user's selected time range using the ${__interval} template variable. Compare per-second rate of net usage against per-minute quota limits using the consumer_quota monitored resource. Which PromQL expression should you configure in the dashboard definition?

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An enterprise runs a core business application primarily in its on-premises data center and maintains a warm standby disaster recovery environment in Google Cloud. The company has established the following technical and business continuity requirements: Primary traffic must be routed entirely to on-premises compute resources during normal operations to minimize cloud compute consumption. If on-premises capacity is exceeded or instances experience an outage, excess traffic must automatically spill over and fail over to autoscaled Managed Instance Groups (MIGs) in Google Cloud. If a backend group degrades and less than 25% of its endpoints pass health checks, traffic must automatically be drained away from it without manual intervention. Which configuration should the Cloud Architect recommend?

Business continuity

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Last updated August 28, 2026

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Professional Cloud DevOps Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Bootstrapping and maintaining a Google Cloud organization
Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads
Applying site reliability engineering practices
Implementing observability practices and troubleshooting issues
Optimizing performance and cost

Ask Scout

Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

An enterprise runs microservices on Google Kubernetes Engine (GKE) and utilizes Cloud Bigtable for real-time transactions. The environment generates 25 TB of application logs and a large volume of Data Access audit logs each month.

The DevOps team must meet the following observability and budget requirements:

Query and join application log data with existing BigQuery business datasets using standard SQL.
Retain application logs for 90 days before deletion.
Eliminate duplicate storage charges and unnecessary ingestion costs from high-volume Bigtable service accounts.

Which strategy should the DevOps team implement to achieve these goals?

Route all application logs to a Cloud Storage bucket with Autoclass enabled, and run a scheduled Cloud Run job every 24 hours to import the log batch into BigQuery.

Create a Log Router sink to stream logs directly to a partitioned BigQuery table with a 90-day partition expiration, set an exclusion filter on the sink for service accounts, and keep the Cloud Logging bucket retention at 30 days.

Exempt the application service accounts in the project's Data Access audit configuration, upgrade a user-defined log bucket to use Log Analytics with a 90-day retention period, and create a linked BigQuery dataset.

Configure field-level access controls on the log bucket to mask PII, extend the default log bucket retention to 90 days, and configure a Log Analytics linked dataset in BigQuery.

Route all application logs to a Cloud Storage bucket with Autoclass enabled, and run a scheduled Cloud Run job every 24 hours to import the log batch into BigQuery.

Create a Log Router sink to stream logs directly to a partitioned BigQuery table with a 90-day partition expiration, set an exclusion filter on the sink for service accounts, and keep the Cloud Logging bucket retention at 30 days.

Exempt the application service accounts in the project's Data Access audit configuration, upgrade a user-defined log bucket to use Log Analytics with a 90-day retention period, and create a linked BigQuery dataset.

What the solution is

This solution combines audit configuration exemptions, custom log bucket retention, and Log Analytics linked datasets to establish a cost-optimized, queryable log lifecycle.

How it meets the requirements

Eliminating unnecessary log ingestion and processing costs: Configuring service account exemptions under the Data Access audit policy prevents Bigtable DATA_READ and DATA_WRITE logs from being generated at the source, preventing unnecessary data transfer and ingestion fees.
Retaining logs for 90 days: Configuring a 90-day custom retention period on the user-defined log bucket satisfies compliance and analytical requirements directly within Cloud Logging.
Enabling SQL joins without double storage: Upgrading the bucket to Log Analytics (which has no upgrade fee) and provisioning a linked BigQuery dataset allows BigQuery Studio and SQL queries to read log data in-place. This completely avoids routing duplicate copies of log records into BigQuery native table storage.

Key advantages and capabilities

Zero data duplication: BigQuery queries query the underlying Cloud Logging storage directly via read-only metadata links.
Full SQL capability: Developers and data analysts can run complex analytical SQL queries joining LogEntry records with relational tables in BigQuery.
Source-level suppression: Audit exemptions prevent log generation altogether rather than dropping them downstream after resource consumption has already occurred.
Centralized retention: Log retention is managed through a single policy on the log bucket rather than synchronizing multiple database partition expiration rules.

Why this is optimal

Alternative approaches that rely on export sinks duplicate log records into both Cloud Logging buckets and BigQuery storage tables, doubling ingestion and storage expenses. Leveraging Log Analytics linked datasets provides full BigQuery analytical capabilities on a single copy of log data.

Configure field-level access controls on the log bucket to mask PII, extend the default log bucket retention to 90 days, and configure a Log Analytics linked dataset in BigQuery.