PCDOE Implementing service monitoring strategies Practice Test 2 — 15 Questions

Question 1

Your organization runs a critical e-commerce platform on Google Kubernetes Engine (GKE). The platform uses Cloud Service Mesh (Anthos Service Mesh) for traffic management and Cloud Monitoring for observability. Recently, after a new release, you observe that the p99 latency of the checkout service has increased from 200ms to 2s. The service's CPU and memory metrics appear normal, and there are no error logs. The release included a change to the Istio VirtualService configuration that added a retry policy: 3 retries with a 500ms timeout per retry. You suspect that the retries are contributing to the latency increase. You want to use Cloud Monitoring to confirm this hypothesis. Which approach should you take?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use Metrics Explorer to query the istio.io/service/server/request_count metric, filtered by response_code_class and destination_service, and include the istio.io/service/server/request_retries metric to see retry counts alongside latency. It directly correlates retry attempts with latency by querying the `istio.io/service/server/request_retries` metric alongside the `istio.io/service/server/request_count` metric in Metrics Explorer. This allows you to visualize the retry count per destination service (checkout) and compare it with the p99 latency increase, confirming whether the retry policy is causing the observed latency spike. The retry policy (3 retries with 500ms timeout) can add up to 1.5s of additional latency per request, which aligns with the increase from 200ms to 2s.

Answer

Use Cloud Trace to analyze distributed traces for the checkout service and look for retry spans

Answer

Check the 'Services' dashboard in Cloud Monitoring, which shows a pre-built latency chart for all services

Answer

Use Logs Explorer to search for logs containing 'retry' in the checkout service namespace

Question 2

You are a DevOps engineer for a SaaS company that provides a REST API. The API is deployed on Google Cloud Run. You have configured Cloud Monitoring alerts for 5xx errors. Recently, you received an alert that the error rate exceeded 5% for 5 minutes. You investigated and found that the errors were HTTP 503 (Service Unavailable) from a specific endpoint. The endpoint calls an internal Cloud SQL database. The database CPU utilization was at 90% during that period. You suspect the database is the bottleneck. Which action should you take to reduce the error rate without over-provisioning?

Accepted Answer

Implement connection pooling and retry logic with exponential backoff in the API service. Implementing connection pooling and retry logic with exponential backoff directly addresses the database bottleneck without over-provisioning. Connection pooling reduces the number of concurrent connections to Cloud SQL, lowering CPU contention, while exponential backoff prevents thundering herd retries that could further overwhelm the database. This approach optimizes existing resources rather than scaling infrastructure.

Answer

Increase the max instances per revision in Cloud Run to handle more concurrent requests

Answer

Reduce the min instances of Cloud Run to decrease load on the database

Answer

Add a Cloud SQL read replica and route read queries to it

Question 3

A company uses Cloud Run for a critical service and needs to set up alerting for 5xx errors. They want to receive a notification within 1 minute of the error rate exceeding 1% for any 1-minute window. Which alerting approach should they use?

Accepted Answer

Create a Cloud Monitoring alerting policy using the 'Request count' metric with a condition that compares the ratio of 5xx responses to total requests over a 1-minute window.. Cloud Monitoring's alerting policies can directly compute the ratio of 5xx responses to total requests using the 'Request count' metric with a custom ratio condition over a 1-minute window. This approach meets the 1-minute notification latency requirement without additional infrastructure, as Cloud Monitoring evaluates the metric every 60 seconds and triggers alerts based on the sliding window.

Answer

Set up a log-based metric for 5xx responses and create an alert on the metric.

Answer

Create a Cloud Logging sink to a Pub/Sub topic and trigger a Cloud Function that sends notifications.

Answer

Use Cloud Monitoring's log-based alerting to trigger on every 5xx log entry.

Question 4

Which TWO are best practices for implementing service monitoring strategies in Google Cloud?

Accepted Answer

Monitor the four golden signals (latency, traffic, errors, saturation) for every service.. A is correct because the four golden signals (latency, traffic, errors, saturation) are the foundational metrics recommended by Google SRE practices for monitoring any service. Monitoring these signals provides a comprehensive view of service health and user experience, enabling rapid detection of issues like high latency or resource exhaustion.

Answer

Rely solely on synthetic monitoring to measure user experience.

Answer

Use multiple monitoring tools to cover all aspects of the system.

Answer

Manually analyze logs and metrics to identify issues.

Question 5

Order the steps to configure a VPC Network Peering between two projects.

Accepted Answer

Create VPC networks in both projects, then configure firewall rules to allow traffic between the VPCs, then create a peering connection from Project A to Project B, then create a peering connection from Project B to Project A, then verify the peering connection.. The correct sequence for configuring VPC Network Peering is to first create the VPC networks in both projects, then configure firewall rules to allow traffic, then create the peering connection in both directions (from Project A to Project B and from Project B to Project A), and finally verify connectivity. Common mistakes include performing steps out of order or omitting the reverse peering.

Answer

Create VPC networks in both projects, then create peering from A to B, then create peering from B to A, then configure firewall rules, then verify.

Answer

Create VPC networks in both projects, then create peering from A to B, then configure firewall rules, then verify.

Answer

Configure firewall rules, then create VPC networks in both projects, then create peering from A to B, then create peering from B to A, then verify.

Question 6

Match each Google Cloud tool to its function in incident management.

Accepted Answer

Cloud Logging: Collect and store log data from applications and services.. In incident management, Cloud Logging collects logs, Cloud Monitoring provides metrics and alerts, Error Reporting aggregates errors, and Cloud Trace traces requests. Common confusions include mixing Cloud Logging with monitoring or debugger functions.

Answer

Cloud Logging: Provide visibility into performance and health.

Answer

Cloud Debugger: Store and analyze logs.

Question 7

A DevOps team is defining an SLO for a web application that runs on Compute Engine behind an HTTP Load Balancer. They need to measure the proportion of requests that complete within 300ms. Which Cloud Monitoring metric is most appropriate as the SLI?

Accepted Answer

loadbalancing.googleapis.com/https/total_latencies. The SLI must measure the proportion of requests completing within 300ms, which is a latency distribution metric. The `total_latencies` metric from the HTTP Load Balancer provides a histogram of request latencies, allowing you to compute the percentage of requests below a threshold (e.g., 300ms). This directly supports the SLO definition.

Answer

loadbalancing.googleapis.com/https/backend_request_bytes

Answer

loadbalancing.googleapis.com/https/frontend_tcp_rtt

Answer

loadbalancing.googleapis.com/https/request_count

Question 8

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 9

You are designing a monitoring strategy for a microservices application running on Google Kubernetes Engine (GKE). You need to create a custom metric that counts the number of failed login attempts from the application logs. The logs are in JSON format and contain a field 'status' with value 'FAILED'. Which approach should you use?

Accepted Answer

Configure a logs-based metric in Cloud Logging that filters for the condition and counts.. A logs-based metric in Cloud Logging directly counts occurrences of a specific log entry pattern (e.g., 'status' = 'FAILED') without requiring additional agents or data exports. This approach is purpose-built for deriving metrics from log data and integrates seamlessly with Cloud Monitoring for alerting and dashboards.

Answer

Use Cloud Monitoring's Metrics Explorer to create a metric from logs using a filter.

Answer

Install the Ops Agent on GKE nodes to collect application metrics directly.

Answer

Export logs to BigQuery and then create a custom metric from the exported data.

Question 10

A team needs to monitor the availability of an HTTPS endpoint that requires a Bearer token in the request header. What is the simplest way to configure this with Cloud Monitoring?

Accepted Answer

Configure the Uptime Check to include a custom header with the Bearer token.. Cloud Monitoring's Uptime Checks natively support custom HTTP headers, including Authorization headers with Bearer tokens. This allows you to directly monitor an authenticated HTTPS endpoint without any additional infrastructure, scripting, or log-based workarounds. It is the simplest and most straightforward configuration for this requirement.

Answer

Deploy a sidecar container that handles the authentication and exposes a plain endpoint.

Answer

Use a synthetic monitor from Cloud Monitoring that handles authentication via a script.

Answer

Export the endpoint logs to Cloud Logging and set up a log-based metric for availability.

Question 11

You are creating a Cloud Monitoring dashboard to display the 99th percentile latency of your HTTP Load Balancer over the last 6 hours. Which MQL query should you use?

Accepted Answer

fetch loadbalancing.googleapis.com/https/total_latencies | align percentile(99). It uses the correct metric type (`total_latencies`) and the proper MQL function `percentile(99)` to compute the 99th percentile latency. The `fetch` statement targets the exact Cloud Monitoring metric for HTTPS load balancer latencies, and `align percentile(99)` aggregates the raw latency distribution data over the specified time window (last 6 hours) to produce the desired percentile value.

Answer

fetch https_lb_rule :: latency | align 99p

Answer

fetch loadbalancing.googleapis.com/https/request_count | align | ratio

Answer

fetch loadbalancing.googleapis.com/https/total_latencies | align 99 | with latency

Question 12

A company has on-premises servers running Linux and GKE clusters. They want to monitor all infrastructure using Cloud Monitoring. Which solution is most scalable and aligned with Google's best practices?

Accepted Answer

Use Google's managed service for Prometheus on GKE and a Prometheus federation for on-prem.. It leverages Google's managed service for Prometheus on GKE, which is fully integrated with Cloud Monitoring and eliminates the operational overhead of self-managing Prometheus. For on-premises servers, Prometheus federation allows scraping metrics from the on-prem Prometheus instance and forwarding them to the managed service, providing a unified, scalable monitoring solution that aligns with Google's best practices for hybrid environments.

Answer

Use collectd on on-prem servers to send to Cloud Monitoring via the Stackdriver agent configuration.

Answer

Deploy Prometheus on both environments and use the PromQL adapter for Cloud Monitoring.

Answer

Install the Ops Agent on all on-prem servers and use Google's default GKE monitoring.

Question 13

An application running on App Engine is throwing exceptions. The DevOps team wants to be notified when a new type of exception appears. Which Cloud Monitoring feature should they use?

Accepted Answer

Error Reporting. Error Reporting is the correct choice because it is a Cloud Monitoring feature specifically designed to count, analyze, and increase the severity of exceptions in application logs. It automatically groups exceptions by type and can trigger notifications when a new type of exception (one not seen before) appears, which directly meets the DevOps team's requirement.

Answer

Uptime Checks

Answer

Custom alerts

Answer

Logs-based metrics

Question 14

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 15

A company wants to reduce costs associated with Cloud Monitoring. They have many custom metrics and high ingestion rates. Which cost optimization strategy is most effective?

Accepted Answer

Delete all unused custom metrics and reduce labels.. Deleting unused custom metrics and reducing labels directly reduces the volume of data ingested and stored, which is the primary cost driver in Cloud Monitoring (formerly Stackdriver). Custom metrics incur charges per data point ingested, and each unique label combination creates additional time series, multiplying costs. This strategy eliminates waste without sacrificing necessary monitoring fidelity.

Answer

Use log-based metrics instead of custom metrics.

Answer

Aggregate metrics into buckets and export to BigQuery for analysis.

Answer

Reduce the sampling rate of all custom metrics to 1 minute.