GCP Professional Cloud Architect Practice Question
Your SRE team will execute a 50 000-requests-per-second load test against a dual-region Cloud Run API using an Apache JMeter cluster on Compute Engine VMs. After each test, engineers need to run BigQuery SQL joins that correlate JMeter sample results with Cloud Run request and error logs to verify latency SLOs and autoscaling behavior. The application containers must remain unchanged and no extra agents may run inside Cloud Run. What is the most appropriate design?
Expose Prometheus metrics from the Cloud Run containers, ingest them as custom metrics in Cloud Monitoring, and modify JMeter to publish its results through the Cloud Monitoring API so that SREs can query the data with PromQL.
Embed Cloud Logging agents inside the Cloud Run containers to forward logs to Looker Studio, and have JMeter upload its CSV report to the same dashboard for visual comparison.
After the test, run gcloud logging read to download Cloud Run logs to a workstation, merge them with JMeter CSV files using a script, and then load the combined data into BigQuery for analysis.
Configure a Cloud Logging sink to export Cloud Run request and error logs to BigQuery, and install an open-source BigQuery backend listener plugin in JMeter to write each test sample to another table in the same dataset for immediate correlation.
Create a Cloud Logging sink that streams Cloud Run request and error logs to a BigQuery dataset with latency measured in seconds. On the JMeter VMs, add an open-source BigQuery backend listener plugin so that every load-test sample is written to a separate table in the same dataset during the test. Because only the JMeter hosts require the plugin and Cloud Run already exports logs automatically, no modifications or agents are needed in the service. Engineers can immediately run SQL joins across the two tables to confirm latency SLOs. The alternative approaches either introduce unsupported agents into Cloud Run, require manual log handling that delays analysis, or rely on custom metrics that do not provide the required log-level correlation.
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What is a Cloud Logging sink, and how does it work?
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What is the function of the BigQuery backend listener plugin?
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Why can't agents or modifications be added to Cloud Run in this design?
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What is a Cloud Logging sink in GCP?
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What does the BigQuery backend listener plugin for JMeter do?
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Why can't agents or modifications be added to Cloud Run containers in this design?
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GCP Professional Cloud Architect
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