Ensuring solution and operations excellence — PCA Practice Quiz

Question 1

A retail company runs multiple microservices on GKE and Compute Engine. The analytics team wants to run ad hoc SQL queries across the last 18 months of application and audit logs to identify usage trends and correlate errors with business events. Logs are currently retained only in Cloud Logging's default buckets. The platform team needs a low-maintenance, cost-effective way to make this historical log data queryable at scale without impacting the running services. What should they recommend?

Accepted Answer

Create an aggregated log sink at the organization level that exports the relevant logs to a BigQuery dataset, and set an appropriate table partition expiration for 18-month retention.

Answer

Increase the retention period of the default Cloud Logging bucket to 18 months and have analysts use the Logs Explorer query interface for SQL-style analysis.

Answer

Export logs to Cloud Storage via a log sink and have analysts download and parse the JSON files with a custom script whenever they need to run analysis.

Answer

Install the Ops Agent on every VM and node to forward logs to a self-managed Elasticsearch cluster on Compute Engine for querying.

Question 2

A financial services company centralizes all application and audit logs in Cloud Logging. Their compliance team requires that audit logs be retained for 7 years and remain queryable within Cloud Logging, while high-volume debug logs from development workloads only need 14 days of retention to minimize cost. The platform team wants to manage these retention differences without exporting logs to external systems or writing custom deletion scripts. What should they do?

Accepted Answer

Route audit logs to a dedicated log bucket with a 7-year custom retention period, and route debug logs to a separate log bucket with a 14-day retention period using log sinks with inclusion filters.

Answer

Keep all logs in the _Default log bucket and set its retention to 7 years, then create a scheduled Cloud Function to delete debug log entries older than 14 days.

Answer

Export all logs to Cloud Storage buckets and apply Object Lifecycle Management rules to enforce the different retention windows for each log type.

Answer

Export audit logs to BigQuery with a 7-year table expiration and keep debug logs only in the _Required bucket, relying on its default retention.

Question 3

A payments team runs a critical API on GKE. They receive frequent false-positive pages: alerts fire whenever CPU briefly spikes, even though the service remains healthy and responsive. The team wants to only be paged when the service is genuinely degraded — specifically, when high request latency AND an elevated error rate occur together over a sustained window. What is the most appropriate way to configure this in Cloud Monitoring?

Accepted Answer

Create a single alerting policy with multiple conditions joined by a logical AND, using a rolling window (e.g., 5 minutes) on the latency and error-rate metrics so the policy triggers only when both conditions are simultaneously met.

Answer

Create two separate alerting policies — one for latency and one for error rate — each with its own notification channel, so both pages arrive together.

Answer

Create a single alerting policy with the latency and error-rate conditions joined by a logical OR so any signal of trouble triggers a page.

Answer

Lower the CPU alerting threshold and add a longer duration window so transient CPU spikes are filtered out before paging.

Question 4

Your SRE team manages a customer-facing payments API running on GKE. Leadership has approved a 99.9% availability SLO measured over a rolling 28-day window. The team wants an automated, low-noise way to be alerted while there is still enough error budget remaining to react, rather than only after the SLO is already breached. Which approach in Google Cloud Observability best meets this requirement?

Accepted Answer

Configure a Cloud Monitoring SLO with multi-window, multi-burn-rate alerting policies that fire on fast and slow error-budget consumption.

Answer

Create a single threshold alert that fires whenever the 28-day availability metric drops below 99.9%.

Answer

Set up an uptime check that pings the API every minute and pages the on-call engineer on any single failed probe.

Answer

Build a Cloud Monitoring dashboard showing request success rate and have engineers visually review it during business hours.

Question 5

A retail company runs dozens of microservices across several GKE clusters and Compute Engine instances, all managed by different teams under a single Google Cloud project. The SRE team wants a way to organize related resources so that dashboards, alerting policies, and health views can be scoped to specific applications (e.g., 'checkout', 'catalog') without manually listing every resource each time infrastructure is added or removed. New instances are created and destroyed frequently by autoscaling. What is the most operationally efficient approach in Cloud Monitoring?

Accepted Answer

Create Cloud Monitoring resource groups defined by dynamic membership criteria such as labels, tags, or naming patterns, then scope dashboards and alerts to those groups.

Answer

Manually add each resource to a static list in each dashboard and alerting policy, updating the list whenever autoscaling changes the fleet.

Answer

Export all metrics to BigQuery and build per-application views using scheduled queries that join on resource IDs.

Answer

Split each application into its own Google Cloud project so that monitoring is naturally scoped per project.

Question 6

A retail company runs several microservices on Cloud Run. During a recent outage, the SRE team spent hours manually correlating request logs by trace ID because they had to export logs to BigQuery first and wait for the scheduled sink to run. The team wants the ability to run ad hoc SQL-style queries directly against their existing Cloud Logging log buckets during incidents, without setting up an export pipeline or duplicating storage. Which approach best meets this requirement?

Accepted Answer

Upgrade the relevant log buckets to use Log Analytics, then query them using the SQL interface in the Log Analytics page or a linked BigQuery dataset.

Answer

Create a new aggregated log sink that continuously streams all logs to a dedicated BigQuery dataset for real-time querying.

Answer

Enable Cloud Trace sampling at 100% so all request logs are automatically indexed and searchable during incidents.

Answer

Increase the log bucket retention period to 400 days so historical logs remain queryable through the Logs Explorer search bar.

Question 7

A payment processing service runs on a Managed Instance Group behind an internal load balancer. Latency spikes occur when the queue of pending transactions grows, but CPU utilization stays low because the bottleneck is a downstream dependency, not compute. The operations team wants the MIG to scale based on the actual pending-transaction backlog rather than CPU. What should they implement to drive autoscaling on this business signal?

Accepted Answer

Publish the pending-transaction count as a custom metric to Cloud Monitoring and configure the MIG autoscaler to scale on that custom metric.

Answer

Increase the CPU target utilization threshold on the existing CPU-based autoscaling policy so instances scale sooner.

Answer

Configure an uptime check against the service and add instances manually whenever the check reports elevated latency.

Answer

Enable predictive autoscaling based on historical CPU patterns to add capacity before latency spikes occur.

Question 8

A company runs 12 separate Google Cloud projects, one per business unit, each with its own Compute Engine and Cloud SQL workloads. The central SRE team needs a single place to build dashboards and alerting policies that span metrics from all 12 projects, without duplicating configuration in each project. They also want to keep each project's IAM and billing independent. What is the recommended approach?

Accepted Answer

Create a metrics scope in a dedicated scoping project and add all 12 projects as monitored projects, then build dashboards and alerts from that scoping project.

Answer

Configure a log sink in each project that exports metrics to a central BigQuery dataset and build dashboards from BigQuery.

Answer

Move all 12 projects under a single parent project so their metrics are automatically merged into one monitoring view.

Answer

Enable a Cloud Monitoring workspace in each project and manually replicate the same dashboards and alert policies across all 12.

Question 9

A retail company runs a payment API on Google Kubernetes Engine. During recent incidents, on-call engineers received alerts but wasted valuable time searching wikis and chat threads to find the correct diagnostic steps for each specific alert. The SRE lead wants each Cloud Monitoring alert notification to arrive already containing a direct link to the specific troubleshooting documentation for that alerting condition, without building a custom middleware service. What should the SRE lead do?

Accepted Answer

Add the documentation content and a URL to the alerting policy's Documentation field so the runbook link is included automatically in every notification for that policy.

Answer

Create a Pub/Sub notification channel and write a Cloud Function that appends the relevant runbook URL before forwarding the alert to email.

Answer

Configure a log-based metric that captures the runbook URL as a label and attach it to the alerting policy's threshold condition.

Answer

Store the runbook links in a Cloud Storage bucket and grant on-call engineers Viewer access so they can look them up when an alert fires.

Question 10

You operate a customer-facing payments API on Cloud Run and have defined a 99.9% availability SLO over a rolling 30-day window. Your on-call team complains that they receive too many pages for tiny, transient dips that self-resolve, yet they occasionally miss slow-burning degradations until customers report problems. You want an alerting strategy that pages urgently for fast burns and creates lower-priority tickets for slow burns while minimizing false pages. Which approach should you configure in Cloud Monitoring?

Accepted Answer

Configure multiwindow, multi-burn-rate alerting policies on the SLO error budget — a fast-burn policy over short windows for urgent paging and a slow-burn policy over longer windows for ticketing.

Answer

Create a single threshold alert that fires whenever the request error rate exceeds 0.1% for one minute.

Answer

Set an uptime check every 60 seconds against the API and page the on-call team whenever a single check fails.

Answer

Alert only when the remaining 30-day error budget drops below 10%, paging the on-call team immediately when that occurs.

Question 11

A payments team is defining an SLO for their checkout API using Cloud Monitoring service monitoring. Their stated reliability goal is that 99.5% of requests should complete in under 400 ms, measured over a rolling 28-day period. The team wants the SLO configuration to accurately reflect this goal so error budget calculations and burn-rate alerts behave as expected. Which SLO configuration correctly represents this objective?

Accepted Answer

A request-based, latency-threshold SLI with a 400 ms threshold, a 99.5% goal, and a rolling 28-day compliance period.

Answer

A windows-based availability SLI with a 99.5% goal and a calendar-month compliance period, using uptime checks as the signal.

Answer

A request-based availability SLI counting HTTP 2xx responses with a 99.5% goal and a rolling 7-day compliance period.

Answer

A windows-based latency SLI with a 400 ms threshold, a 99.5% goal, and a calendar 28-day compliance period.

Question 12

Your company runs a customer-facing payment API on Cloud Run. The operations team wants alerts routed differently based on severity: warning-level conditions should create a ticket in their incident management tool, while critical outages must page the on-call engineer immediately via SMS and PagerDuty. Currently all Cloud Monitoring alerting policies send email to a shared distribution list, and minor issues are being missed among the noise. What is the most appropriate way to implement tiered alert routing in Cloud Monitoring?

Accepted Answer

Create separate alerting policies for warning and critical conditions, and attach different notification channels (webhook to the ticketing tool for warnings; SMS and PagerDuty channels for critical) to each policy.

Answer

Keep a single alerting policy for all conditions and configure the shared email distribution list to filter messages into folders based on subject line.

Answer

Export all alert events to a Pub/Sub topic and write a Cloud Function that parses each message and forwards it to the correct destination based on custom logic.

Answer

Increase the alerting policy's duration threshold so that only sustained critical outages generate any notification, reducing the volume of minor alerts.

Question 13

A payments microservice running on Compute Engine has gradually increased its CPU utilization over the past three release cycles, driving up costs and occasionally triggering autoscaling that adds instances during peak traffic. The engineering team suspects inefficient code paths but cannot reproduce the behavior in staging, where load patterns differ. They need continuous, low-overhead visibility into which functions consume the most CPU and heap in the live production environment, correlated across releases, without redeploying instrumented builds each time. Which Google Cloud Observability capability should the architect recommend?

Accepted Answer

Enable Cloud Profiler with the profiling agent to capture statistical CPU and heap profiles continuously from production

Answer

Instrument the service with Cloud Trace to capture end-to-end request latency spans and analyze them per release

Answer

Create log-based metrics from application logs to count expensive function invocations and alert on thresholds

Answer

Configure a Cloud Monitoring dashboard tracking per-instance CPU utilization metrics and compare across releases

Question 14

A retail company runs a microservices platform on GKE and has enabled Cloud Trace to diagnose latency. During a major sales event, engineers report that traces for many slow requests are missing, making it hard to find the bottleneck. The default automatic sampling is capturing only a small fraction of requests under high load. The team wants to increase the likelihood of capturing traces for the slowest requests without a massive increase in ingestion cost or code changes across all services. What should the architect recommend?

Accepted Answer

Configure custom sampling in the instrumentation (e.g., OpenTelemetry) to increase the sampling rate and apply tail-based sampling that prioritizes high-latency and error spans.

Answer

Disable Cloud Trace and rely on Cloud Logging structured logs with latency fields to reconstruct request timing manually.

Answer

Increase the number of GKE nodes so that each service has spare CPU to emit 100% of traces during the event.

Answer

Export all traces to BigQuery and query them there to find slow requests after the event ends.

Question 15

A retail company deploys a customer-facing API to Cloud Run using an automated CI/CD pipeline. During recent releases, several bad revisions reached 100% of traffic before the on-call team noticed elevated 5xx errors, causing customer impact. The architect wants to reduce the blast radius of faulty releases without requiring an engineer to manually watch dashboards after every deployment. Which approach best achieves this?

Accepted Answer

Deploy each new revision with no traffic, then use a pipeline step that shifts a small percentage of traffic and evaluates error-rate and latency SLO metrics before automatically promoting or aborting the rollout.

Answer

Increase the Cloud Run maximum instance count so the service can absorb more requests and mask transient errors during deployment.

Answer

Configure a Cloud Monitoring alert policy that pages the on-call engineer whenever the 5xx rate exceeds a threshold after full deployment.

Answer

Enable Cloud Run request retries so failed requests are automatically retried against the new revision until they succeed.

Question 16

A retail company runs a fleet of stateless microservices on Cloud Run behind an external HTTPS load balancer. During peak sale events, the operations team notices that a small percentage of user requests intermittently fail with elevated latency, but standard dashboards show all services as healthy and within their SLOs. Leadership wants a way to continuously verify the complete end-to-end user journey (login, browse, add-to-cart, checkout) from an external perspective and be alerted the moment any step of that journey degrades, even when individual service metrics look normal. Which approach best meets this requirement?

Accepted Answer

Create Cloud Monitoring synthetic monitors that script the full multi-step user journey and alert on failures or latency breaches in the workflow.

Answer

Configure Cloud Monitoring uptime checks against each service's health endpoint and alert on individual endpoint availability.

Answer

Enable Cloud Trace with 100% sampling and build an alert policy on the p99 latency of each individual span.

Answer

Export request logs to BigQuery and schedule an hourly query that flags journeys exceeding a latency threshold.

Question 17

A SaaS company runs a customer-facing application on Cloud Run. After a recent release, support tickets spike with users reporting intermittent failures, but the operations team is struggling to identify which code defects are responsible because individual log entries are scattered across thousands of requests. The team wants a low-effort way to automatically group recurring application exceptions, see how often each distinct fault occurs, track whether a fault is new or regressed after a deploy, and get notified when a new error type appears. Which Google Cloud Observability capability should they adopt?

Accepted Answer

Enable Error Reporting, which automatically aggregates and deduplicates stack traces into distinct error groups with occurrence counts and new-error notifications.

Answer

Create a log-based metric that counts all log entries with severity ERROR and build a Cloud Monitoring dashboard from it.

Answer

Configure Cloud Trace with 100% sampling to capture every request span and inspect latency waterfalls for failures.

Answer

Export all Cloud Logging entries to BigQuery and write scheduled queries that GROUP BY the raw message text.

Question 18

A payments team runs a critical microservice on Cloud Run. The application writes structured JSON logs to Cloud Logging, and certain business failures are logged with a field severity=ERROR and a jsonPayload field txn_status="declined_downstream". Operations wants to be alerted whenever the rate of these specific downstream-decline errors exceeds a threshold within a rolling window, without changing application code and without exporting logs to an external system. What is the most appropriate approach?

Accepted Answer

Create a log-based metric (counter) filtered on the specific jsonPayload field and severity, then build a Cloud Monitoring alerting policy on that metric's rate over a rolling window.

Answer

Configure a log sink to export all logs to BigQuery and schedule a query that emails the team when the decline count is high.

Answer

Enable Error Reporting and rely on its automatic grouping to notify the team when the decline error group grows.

Answer

Set up a log sink to Pub/Sub and write a Cloud Function that parses each message and calls an alerting webhook when a decline is detected.

Question 19

A retail company runs its checkout service on a regional managed instance group (MIG) behind an external HTTP(S) load balancer. The team uses a rolling update to deploy new instance template versions. During a recent release, error rates spiked several minutes after the update completed, and operators had to manually recreate instances from the previous template, extending the outage. The architect wants a release process that automatically limits exposure to a bad version and enables fast, low-risk rollback with minimal manual steps. What should the architect recommend?

Accepted Answer

Use a canary rolling update by adding the new instance template as a second version in the MIG with a small target size, monitor health, and increase the new version's size only after validation—rolling back by removing the canary version.

Answer

Increase the MIG's maxUnavailable to 100% so the rolling update replaces all instances at once, reducing the time the two versions coexist and simplifying rollback.

Answer

Disable autohealing during deployments so that faulty new instances are not automatically recreated, allowing operators more time to investigate before rolling back.

Answer

Deploy the new template to a separate unmanaged instance group and manually swap the load balancer backend once testing is complete.

Question 20

A microservices application on GKE writes logs to stdout as plain text lines. During incidents, engineers struggle to filter by severity and cannot correlate log entries belonging to the same user request across services. The team wants to improve log usability in Cloud Logging with minimal application rework and without deploying additional log-shipping agents. What should you recommend?

Accepted Answer

Emit logs as single-line JSON with fields such as severity and a trace/request identifier so Cloud Logging parses them into structured LogEntry fields for filtering and correlation.

Answer

Configure a log sink to export all container logs to BigQuery and have engineers write SQL queries to filter by severity and group by request.

Answer

Deploy a Fluentd DaemonSet with custom grok regex parsers to extract severity and request IDs from the plain-text log lines.

Answer

Increase the log retention period in the _Default bucket and rely on the Logs Explorer full-text search to locate related entries.

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