PCDOE Practice Test 22 — 15 Questions

Question 1

A company uses Error Budgets for their service. The SLO is 99.9% availability over a 30-day window. The service has been down for 30 minutes in the current window. What is the remaining error budget?

Accepted Answer

13.2 minutes. The SLO of 99.9% over a 30-day window allows a total error budget of 43.2 minutes (30 days × 24 hours × 60 minutes × 0.001). The service has already consumed 30 minutes of downtime, so the remaining error budget is 43.2 - 30 = 13.2 minutes. Option C is correct because it reflects this precise calculation.

Answer

43.2 minutes

Answer

60 minutes

Answer

30 minutes

Question 2

A DevOps engineer receives an alert that the error budget for a critical service has been exhausted. The service runs on Compute Engine behind an HTTP(S) load balancer. The team wants to reduce the impact on users while investigating. What should the engineer do first?

Accepted Answer

Roll back the most recent deployment. Rolling back the most recent deployment is the correct first action because it immediately restores the service to a known stable state, stopping further consumption of the error budget. This aligns with the incident management principle of 'mitigate first, investigate later' — reducing user impact takes priority over root cause analysis. The HTTP(S) load balancer will automatically route traffic to the previous healthy version once the rollback is complete.

Answer

Begin a detailed postmortem analysis

Answer

Disable the alerting policy to reduce noise

Answer

Increase the number of instances in the managed instance group

Question 3

A site reliability engineer is defining SLOs for a microservice application running on Google Kubernetes Engine. The application serves user-facing API requests. Which TWO approaches should the engineer take to effectively monitor the service's performance?

Accepted Answer

Monitor the 99th percentile of request latency directly using Cloud Monitoring custom metrics.. Option C is correct because SLOs for user-facing APIs should be based on the actual user experience, and the 99th percentile of request latency captures tail latency that averages hide; Cloud Monitoring custom metrics let you record and alert on this distribution directly from the application. Option D is correct because error rate is a core SLI for availability/reliability, and logs-based metrics can count HTTP 5xx responses emitted by the service, turning log entries into a measurable error-rate signal for SLO tracking. Option A is not appropriate because average latency masks tail behavior and can look healthy while many users experience slow responses. Option B is wrong because container CPU utilization is a resource metric, not a direct measure of request latency or user-perceived performance. Option E is wrong because the number of running pods reflects capacity/scaling state, not service quality or user-facing SLO attainment.

Answer

Monitor average latency because it is most representative of typical user experience.

Answer

Monitor container CPU utilization as a proxy for application latency.

Answer

Use the number of running pods as the primary SLO indicator.

Question 4

A team uses Cloud SQL for MySQL. They have read-heavy traffic and want to reduce costs. Which strategy is most effective?

Accepted Answer

Use read replicas to offload read queries.. Read replicas offload read queries from the primary instance, allowing the primary to focus on writes and reducing the load on the more expensive primary instance. This is the most effective cost-reduction strategy for read-heavy workloads because read replicas can be smaller or use cheaper tiers, and they scale horizontally. Offloading reads also improves performance without increasing the primary's size.

Answer

Use committed use discounts.

Answer

Use High Availability with a standby instance.

Answer

Use vertical scaling to increase instance size.

Question 5

Your application uses Cloud SQL for MySQL and you notice that read replica lag is increasing. Which action would most likely reduce replica lag?

Accepted Answer

Increase the machine type of the replica.. Increasing the machine type of the replica (Option C) directly addresses read replica lag by providing more CPU and memory resources to the replica instance. This allows the replica to apply binary log (binlog) events from the primary faster, reducing the replication lag. Cloud SQL for MySQL uses asynchronous replication, so a replica with insufficient resources cannot keep up with the write throughput of the primary.

Answer

Configure automatic failover to the replica.

Answer

Decrease the memory of the primary instance.

Answer

Promote the replica to a standalone instance.

Question 6

Refer to the exhibit. What does the alert condition indicate?

Accepted Answer

It alerts for any Cloud Run revision that has more than 1000 requests in a 1-minute window.. The alert condition in the exhibit uses a per-revision metric (e.g., `run.googleapis.com/request_count`) with a threshold of 1000 and a 1-minute window. This means the alert fires for any individual Cloud Run revision that exceeds 1000 requests within that window, not for the aggregate across all revisions. Option B correctly identifies this per-revision behavior.

Answer

It alerts when the request count drops below 1000 for 1 minute.

Answer

It alerts when the average request count across all revisions exceeds 1000 over 1 minute.

Answer

It alerts when the total request count across all revisions exceeds 1000 per minute.

Question 7

A DevOps team is setting up a Google Cloud organization. They want to centralize logging and monitoring across all projects. What is the recommended approach?

Accepted Answer

Create a dedicated project for logging and monitoring, and configure all other projects to send logs and metrics to that project.. The recommended approach for centralizing logging and monitoring in a Google Cloud organization is to create a dedicated project that acts as a log sink and metric aggregator. By configuring aggregated sinks at the organization or folder level, all projects automatically forward logs to the dedicated project's Cloud Logging bucket, and metrics can be collected via the Monitoring API. This ensures a single pane of glass for operations without manual per-project setup.

Answer

Enable logging and monitoring in each project individually and use the Cloud Console to view them.

Answer

Enable Cloud Audit Logs in the organization and view them from the Organization level.

Answer

Use Stackdriver (now Operations) to aggregate logs from all projects automatically.

Question 8

A multinational company runs an application on Google Cloud with an SLO of 99.99% monthly availability. They use a multi-region deployment with Cloud Load Balancing and Cloud Spanner. During a regional outage in us-central1, traffic fails over to us-east1. However, the incident response team is not alerted because the error budget burn rate remained below the alert threshold. What should the team change to ensure timely alerting for such regional failures?

Accepted Answer

Create a custom dashboard and alert for regional unavailability using Cloud Monitoring metrics like load_balancing/backend_request_count and region health checks.. The incident response team is not alerted due to the error budget burn rate remaining below the threshold, even though a regional outage occurred. By creating a custom dashboard and alert for regional unavailability using Cloud Monitoring metrics like `load_balancing/backend_request_count` and region health checks, the team can directly monitor the health of each region and trigger alerts immediately when a region becomes unhealthy, bypassing the error budget burn rate logic that only triggers when the overall SLO is at risk.

Answer

Shorten the SLO compliance window from 30 days to 7 days.

Answer

Change the SLO to 99.9% to allow more error budget.

Answer

Reduce the error budget burn rate alert threshold from 10% to 5% per hour.

Question 9

A company is transferring large datasets from on-premises to Google Cloud using a VPN. They notice high latency due to packet loss. What is the most effective way to improve throughput?

Accepted Answer

Set up Dedicated Interconnect for a more reliable connection.. Dedicated Interconnect provides a direct, private physical connection between on-premises and Google Cloud, bypassing the public internet entirely. This eliminates the packet loss and high latency inherent in VPN tunnels over the internet, offering consistent throughput and lower latency for large dataset transfers.

Answer

Enable compression on the VPN tunnel.

Answer

Increase the number of VPN tunnels and use BGP multipath.

Answer

Use a multi-region GCP endpoint and distribute traffic.

Question 10

A company uses BigQuery on-demand pricing. They want to reduce costs without changing usage patterns. Which THREE strategies are effective? (Choose three.)

Accepted Answer

Use clustered tables.. Option A (Use clustered tables) is correct because clustering physically sorts data by the specified columns, so queries with filters or aggregations on those columns scan fewer bytes, and BigQuery on-demand pricing bills by bytes processed. Option B (Use materialized views) is correct because they precompute and cache query results, so repeated queries against the materialized view read far less data than re-scanning the base tables, directly lowering on-demand bytes-billed. Option E (Use partitioned tables) is correct because partitioning lets BigQuery prune irrelevant partitions, so queries filtering on the partition column scan only the needed partitions instead of the whole table. Option C (legacy SQL) is not correct because legacy SQL does not inherently reduce bytes processed and standard SQL is the recommended, more capable dialect. Option D (flat-rate pricing) is not correct because it changes the pricing model to reserved slots rather than reducing cost under the existing on-demand model, and the scenario specifies on-demand pricing without changing usage patterns.

Answer

Use legacy SQL instead of standard SQL.

Answer

Use flat-rate pricing.

Question 11

Which TWO actions can reduce tail latency in a microservices architecture deployed on GKE? (Choose 2)

Accepted Answer

Run multiple replicas of each service and use a load balancer with least-request algorithm.. Option A is correct because running multiple replicas of each service removes single-pod queuing bottlenecks, and a load balancer using the least-request algorithm routes new requests to the replica with the fewest in-flight requests, which directly reduces the chance that a request lands behind a long queue and experiences tail latency. Option E is correct because request hedging sends duplicate requests to multiple replicas and returns the first successful response, so a slow or straggling replica no longer determines the tail latency for that request; this is a standard technique for cutting p99 latency at the cost of extra load. Option B is not correct because a single large machine type for all services does not address per-request queuing or straggler replicas and can create noisy-neighbor and scaling problems. Option C is not correct because session affinity pins users to the same pod, which can concentrate load and worsen tail latency rather than reduce it. Option D is not correct because increasing batch size generally increases per-request processing time and can raise tail latency, not lower it.

Answer

Use a single large machine type for all services.

Answer

Enable session affinity to keep users on the same pod.

Answer

Increase the batch size for processing requests.

Question 12

An alerting policy triggers frequently for a spike in CPU utilization on a Compute Engine instance, but the spike lasts only a few seconds. The SRE team wants to reduce false positives. Which change should they make?

Accepted Answer

Increase the evaluation period and duration.. Increasing the evaluation period and duration ensures that the alerting policy only fires when the CPU utilization spike persists over a longer window, filtering out transient spikes that last only a few seconds. This directly reduces false positives by requiring sustained high utilization before triggering an alert, aligning with Google Cloud Monitoring's sliding window evaluation logic.

Answer

Increase the notification channel threshold.

Answer

Decrease the alerting duration to 0s.

Answer

Change the aggregation to mean instead of max.

Question 13

An SRE team needs to implement an incident management workflow that automatically creates a ticket in their ITSM tool when a critical alert fires. They use Cloud Monitoring. Which approach should they use?

Accepted Answer

Use a Cloud Pub/Sub notification channel and a Cloud Function that receives the alert and calls the ITSM API.. Cloud Monitoring can send alert notifications to a Cloud Pub/Sub topic, which then triggers a Cloud Function. The Cloud Function can parse the alert payload and call the ITSM tool's API to create a ticket, providing a reliable, scalable, and decoupled integration that supports custom logic and error handling.

Answer

Configure the alerting policy to send notifications via email to the ITSM system's email-to-ticket feature.

Answer

Create a webhook notification channel directly to the ITSM tool.

Answer

Use the Cloud Monitoring API to periodically pull alerts and create tickets.

Question 14

A company runs a web application on Google Kubernetes Engine (GKE) with multiple services. They want to reduce costs without impacting performance. Which THREE actions should they take? (Choose three.)

Accepted Answer

Enable node auto-provisioning to automatically adjust node pools.. Node auto-provisioning in GKE automatically creates and scales node pools based on the resource requirements of pending pods. This eliminates the need for manual node pool management and ensures that only the necessary compute resources are provisioned, reducing costs without manual intervention or over-provisioning.

Answer

Enable cluster autoscaling and manually scale nodes based on peak load.

Answer

Deploy a service mesh like Istio to optimize traffic routing.

Question 15

A DevOps engineer notices that some Compute Engine instances are not reporting metrics to Cloud Monitoring. Which two potential causes should they investigate? (Choose two.)

Accepted Answer

The instances have insufficient IAM permissions to write metrics.. Compute Engine instances require the appropriate IAM permissions (e.g., roles/monitoring.metricWriter) to write metrics to Cloud Monitoring. Without these permissions, the API calls to ingest metric data are denied, even if the Ops Agent is installed and running.

Answer

The instances are in a different region and Cloud Monitoring doesn't support cross-region.

Answer

The instances are preemptible and automatically stop reporting after 24 hours.

Answer

The instances are in a different project and not peered.