PCD Managing application performance monitoring Practice Test 4 — 15 Questions

Question 1

An application deployed on Google Kubernetes Engine (GKE) is experiencing intermittent high latency. The operations team wants to quickly identify which specific code path is causing the delay. What should they use?

Accepted Answer

Enable Cloud Trace and analyze trace spans.. Cloud Trace is designed specifically for latency analysis in distributed systems like GKE. It captures end-to-end request latency and breaks it down into individual spans, each representing a specific code path or service call. By analyzing these spans, the operations team can pinpoint which exact code path (e.g., a database query, external API call, or internal function) is causing the intermittent high latency.

Answer

Use Cloud Profiler to identify memory leaks.

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Set up a Cloud Monitoring uptime check.

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Review Cloud Logging logs to find error messages.

Question 2

A company runs a stateless application on Compute Engine behind a load balancer. They want to monitor the number of active requests per instance without adding custom instrumentation. What is the most straightforward approach?

Accepted Answer

Use the load balancer's built-in 'request_count' metric.. Option D is correct because the load balancer's built-in 'request_count' metric directly provides the number of active requests per instance without requiring any additional instrumentation or agents. This metric is automatically collected by Cloud Monitoring for Google Cloud HTTP(S) load balancers, making it the most straightforward approach for a stateless application on Compute Engine.

Answer

Configure the Cloud Monitoring agent to collect request metrics.

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Install the Cloud Logging agent and parse access logs.

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Deploy Prometheus and instrument the application.

Question 3

An application writes structured logs to Cloud Logging. The team wants to create a metric based on the value of a JSON field 'order_total' to alert when totals exceed $1000. What type of metric should they use?

Accepted Answer

Log-based metric.. A log-based metric extracts a numeric value from a log entry's JSON payload using a regular expression or a label extractor. By defining a log-based metric on the 'order_total' field and setting an alert threshold of $1000, the team can monitor and alert on high-value orders directly from Cloud Logging without additional instrumentation.

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Uptime check metric.

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Error Reporting metric.

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Custom metric from Cloud Monitoring agent.

Question 4

A team notices that a Cloud Run service occasionally returns HTTP 500 errors. They have enabled Cloud Error Reporting. What is the best way to rapidly diagnose the root cause of these errors?

Accepted Answer

Examine the error group details in Cloud Error Reporting.. Cloud Error Reporting automatically groups similar errors (like HTTP 500s) into error groups, providing stack traces, request details, and occurrence timelines. Examining the error group details is the fastest path to root cause because it surfaces the exact exception and context without requiring manual log filtering or metric setup.

Answer

Search Cloud Logging manually for '500' events.

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View the Cloud Trace dashboard for error traces.

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Create a Cloud Monitoring dashboard for 5xx metrics.

Question 5

A company runs a multi-service application on GKE and wants to create a Service Level Indicator (SLI) for request latency. They have set up Cloud Service Mesh (Anthos Service Mesh) with Istio. Which metric should they use for the SLI?

Accepted Answer

istio_request_duration_milliseconds_bucket metric from Cloud Monitoring.. Option A is correct because `istio_request_duration_milliseconds_bucket` is a native Istio metric automatically exported by Cloud Service Mesh (Anthos Service Mesh) to Cloud Monitoring. It provides a histogram of request latencies, which is the standard data source for building a latency-based SLI (e.g., the proportion of requests under a threshold). This metric is pre-configured and requires no custom instrumentation, making it the most direct and reliable choice for an SLI in this environment.

Answer

Custom metric exported by the application using OpenTelemetry.

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Cloud Trace latency distribution from traces.

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Cloud HTTP Load Balancer latency metric.

Question 6

An operations team is configuring a Cloud Monitoring alerting policy for a critical application. They want to ensure that alerts are only fired when an anomaly persists for at least 5 minutes to reduce noise. Which condition configuration should they use?

Accepted Answer

Use a 'Metric Threshold' condition and set the 'for' parameter to 300 seconds.. Metric absence or evaluation missing period are not for anomaly persistence. Condition threshold with 'for' parameter sets the duration the condition must be true. Never retrigger state is for notification channels.

Answer

Configure 'Evaluation Missing Data' to '5 minutes'.

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Set the condition type to 'Metric Absence' with a 5-minute duration.

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Use 'Never retrigger' state with 5-minute window.

Question 7

A developer wants to automatically capture CPU and memory profiles from a production application running on Compute Engine to identify performance bottlenecks. Which Google Cloud tool should they use?

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct tool because it continuously gathers CPU and memory usage data from production applications with minimal overhead, using statistical sampling to identify which functions or methods consume the most resources. This allows developers to pinpoint performance bottlenecks without adding significant latency or requiring code changes.

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Cloud Logging

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Cloud Monitoring

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Cloud Trace

Question 8

A team uses Cloud Endpoints to manage their API. They want to monitor API latency for each API method. What is the recommended approach?

Accepted Answer

View the built-in Cloud Endpoints latency metrics in Cloud Monitoring.. Cloud Endpoints automatically sends metrics including request latency per method to Cloud Monitoring. Cloud Trace can trace individual requests but not aggregate per method easily. Custom metrics require code changes. Cloud Logging latency is not built-in.

Answer

Parse Cloud Logging endpoint logs to calculate latency.

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Use Cloud Trace to analyze samples and estimate latency.

Answer

Instrument the API code with a custom metric for each method.

Question 9

A company receives a Cloud Monitoring alert that a Compute Engine instance's CPU utilization has exceeded 90% for the past 15 minutes. The incident turns out to be a false alarm caused by a scheduled job that runs daily. How can they prevent future false alarms for this recurring pattern?

Accepted Answer

Create a custom metric that excludes the scheduled job's CPU usage and use it in the alert.. Option A is correct because creating a custom metric that excludes the scheduled job's CPU usage allows the alert to ignore the predictable, recurring spike. This approach uses Cloud Monitoring's custom metrics capability to filter out known noise, ensuring the alert only triggers on unexpected CPU utilization patterns.

Answer

Switch to 'Metric Absence' type condition.

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Increase the CPU utilization threshold to 95%.

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Change the condition's 'for' duration to 30 minutes.

Question 10

Which TWO are best practices for setting up Cloud Monitoring alerting policies to minimize alert fatigue? (Select exactly 2.)

Accepted Answer

Use condition thresholds with an 'AND' combination of multiple metrics.. Using multiple conditions with AND logic reduces false positives. Setting 'for' duration prevents transient spikes from alerting. Aggregating across projects first is not best practice; it's better to alert per project. Using log-based metrics for everything is not always appropriate. Synthetic monitors are for availability, not general alerting.

Answer

Aggregate metrics across all projects before alerting.

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Use log-based metrics for all alerts instead of metric-based alerts.

Answer

Create a separate alerting policy for each possible symptom.

Question 11

Which TWO capabilities does Cloud Service Mesh (Istio) provide to help monitor application performance? (Select exactly 2.)

Accepted Answer

Automatic generation of HTTP request metrics (e.g., request count, latency, error rate) per service.. Option C is correct because Cloud Service Mesh (Istio) automatically generates HTTP request metrics such as request count, latency, and error rate for every service in the mesh. This is achieved through Envoy sidecar proxies that intercept all traffic and export standardized telemetry without requiring any application code changes.

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Legacy Cloud Logging agent integration for container logs.

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Custom Prometheus exporter deployment for each microservice.

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Cloud Endpoints API management with key validation.

Question 12

Which THREE are valid ways to create custom metrics in Cloud Monitoring? (Select exactly 3.)

Accepted Answer

Use the Cloud Monitoring API to write time series directly.. Option B is correct because the Cloud Monitoring API allows you to write time series data directly via the `projects.timeSeries.create` method, which is a primary mechanism for ingesting custom metrics. This enables you to programmatically send metric data from any source, bypassing the need for an agent.

Answer

Use the Cloud Billing pricing calculator to estimate metric costs.

Answer

Deploy Ops Agent with default configuration.

Question 13

A team is investigating increased latency in a web application deployed on Google Kubernetes Engine (GKE). They want to identify which specific service calls are slow. Which Google Cloud tool should they use?

Accepted Answer

Cloud Trace. Cloud Trace is the correct tool because it provides end-to-end latency tracking for requests in distributed systems, including GKE. It captures detailed spans for each service call, allowing the team to pinpoint which specific microservice or API call is causing the increased latency. This aligns directly with the need to identify slow service calls in a web application.

Answer

Cloud Monitoring dashboards

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Cloud Profiler

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Cloud Logging

Question 14

A developer wants to ensure that error logs from their Java application are automatically captured and grouped in Cloud Error Reporting. What is the recommended approach?

Accepted Answer

Instrument the application with the Error Reporting client library. Option C is correct because the Error Reporting client library directly integrates with the application to automatically capture and group error logs, sending them to Cloud Error Reporting without requiring additional infrastructure. This is the recommended approach as it provides structured error reporting with automatic grouping, stack trace analysis, and real-time notifications.

Answer

Configure a log sink to Error Reporting

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Export logs to BigQuery and then import to Error Reporting

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Use a custom log-based metric to count errors

Question 15

An organization has multiple Google Cloud projects for different environments (dev, staging, prod). They want to create a single Cloud Monitoring dashboard that shows metrics from all projects. What is the correct approach?

Accepted Answer

Create a dashboard in a shared project and use metric scopes to include data from other projects. Metric scopes in Cloud Monitoring allow you to view metrics from multiple Google Cloud projects within a single dashboard. By creating a dashboard in a shared (host) project and adding other projects as monitored projects via metric scopes, you can aggregate metrics from dev, staging, and prod environments without duplicating dashboards or exporting data.

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Use a custom metrics export to a central project via log sinks

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Create a separate dashboard in each project and use dashboard sharing

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Use the Monitoring API to aggregate metrics in a single chart