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Professional Cloud Data Engineer
Monitoring the data lake
An enterprise data lake team runs analytical queries against…

Professional Cloud Data Engineer

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Prepare and test your skills

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Sample Question
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Practice Test

Configure Cloud Monitoring Bigquery Performance Alerts

Section: Monitoring the data lake

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Last updated August 27, 2026

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Professional Cloud Data Engineer

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

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

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An enterprise data lake team runs analytical queries against large data lake datasets in BigQuery. The team notices intermittent performance degradation and wants to establish proactive observability to identify bottlenecks and optimize compute resource allocation.

They have the following operational requirements:

Create an automated alerting policy in Cloud Monitoring that triggers when severe query latency outliers occur (specifically, when the 99th percentile of query execution times across a 5-minute rolling window exceeds 60 seconds).
Track slot consumption alongside query execution metrics on a custom dashboard to evaluate whether to adjust capacity commitments and slot assignments.

Which configuration should the team implement in Cloud Monitoring?

Install the Ops Agent on Compute Engine worker VMs, configure an alerting policy on OS Reported CPU % exceeding 80%, and chart the Completed run count on a custom dashboard.

Deploy Synthetic Monitors with broken-link checkers in Cloud Monitoring to probe the BigQuery REST API endpoint, and set up an alert policy on API response codes.

Create an alerting policy selecting the BigQuery Project resource and Query execution times metric, set the time series aggregation to 99th percentile with a 5-minute rolling window, configure a threshold condition greater than 60 seconds, and add the Slot Utilization chart to their dashboard.

Create a log-based metric in Cloud Logging counting all BigQuery job audit logs, set an alerting policy when the total query count exceeds a fixed rate per minute, and monitor BigQuery DTS Config metrics on the dashboard.

Install the Ops Agent on Compute Engine worker VMs, configure an alerting policy on OS Reported CPU % exceeding 80%, and chart the Completed run count on a custom dashboard.

Deploy Synthetic Monitors with broken-link checkers in Cloud Monitoring to probe the BigQuery REST API endpoint, and set up an alert policy on API response codes.

Create an alerting policy selecting the BigQuery Project resource and Query execution times metric, set the time series aggregation to 99th percentile with a 5-minute rolling window, configure a threshold condition greater than 60 seconds, and add the Slot Utilization chart to their dashboard.

What the Cloud Monitoring Configuration Is

Cloud Monitoring natively ingests operational telemetry and performance metrics from BigQuery, allowing administrators to monitor compute consumption, track query latency percentiles, and build customized visualization dashboards.

How it Meets the Stated Requirements

Detect Latency Outliers: By selecting the BigQuery Project resource type and the Query execution times metric, the team configures a metric-threshold condition. Setting the across-time-series aggregation to the 99th percentile over a 5-minute rolling window specifically isolates extreme performance outliers without causing false alarms on standard sub-second queries.
Threshold Alerting: Defining an Above threshold condition with a threshold value of 60 seconds ensures automated incident generation and notification dispatch whenever severe execution delays occur.
Optimize Resource Allocation: Adding the native Slot Utilization chart alongside execution time metrics on a Cloud Monitoring custom dashboard enables engineers to correlate query slowdowns with compute slot saturation, providing the data needed to adjust capacity commitments or reservation assignments.

Key Advantages

Automated Anomaly Detection: Percentile-based aggregation flags high-latency tail events that simple averages or sums would conceal.
Proactive Capacity Planning: Visualizing slot utilization allows teams to distinguish between poorly optimized SQL queries and slot starvation bottlenecks.
Unified Observability: Integrates alerting policies and dashboard widgets within a single pane of glass without external monitoring overhead.

Why This Is Optimal

This approach directly utilizes built-in Google Cloud system metrics and standard statistical aggregators in Cloud Monitoring. It achieves comprehensive query latency and compute slot observability with zero custom agent or data ingestion overhead.

Create a log-based metric in Cloud Logging counting all BigQuery job audit logs, set an alerting policy when the total query count exceeds a fixed rate per minute, and monitor BigQuery DTS Config metrics on the dashboard.