PCD Managing application performance monitoring Practice Test 6 — 15 Questions

Question 1

A developer wants to receive notifications when the error rate of their application exceeds 1% over a 5-minute window. What should they create in Cloud Monitoring?

Accepted Answer

Alerting policy with metric threshold condition. An alerting policy with a metric threshold condition is the correct approach because Cloud Monitoring evaluates a metric (e.g., error rate) against a threshold (1%) over a specified window (5 minutes) and triggers a notification when the condition is met. This directly fulfills the requirement to be notified when the error rate exceeds the threshold, as alerting policies are designed for proactive notification based on metric data.

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Log-based metric

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Dashboard with error rate chart

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Uptime check

Question 2

A team notices that their application's latency has increased after a recent deployment. They suspect a specific code path is slower. Which Google Cloud tool should they use to identify the most time-consuming functions in their code?

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct tool because it performs continuous, low-overhead profiling of CPU and memory usage, pinpointing the specific functions or methods that consume the most resources. Unlike tracing, which focuses on request latency across services, profiling identifies the most time-consuming code paths within a single application process, making it ideal for diagnosing a slow code path after a deployment.

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Cloud Debugger

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Cloud Trace

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Cloud Logging

Question 3

An application running on Compute Engine generates structured logs. The operations team needs to parse a specific field from the logs and create a metric that counts occurrences of a particular value. They want the metric to be available for alerting with minimal delay. What should they do?

Accepted Answer

Create a log-based metric in Cloud Logging. Log-based metrics in Cloud Logging are designed to extract specific fields from structured logs and count occurrences of particular values with near-real-time latency, making them ideal for alerting with minimal delay. They are natively integrated with Cloud Monitoring, so the metric is automatically available for alerting policies without additional infrastructure or data movement.

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Export logs to BigQuery and use scheduled queries

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Write a Cloud Function to process logs from Pub/Sub

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Use the Cloud Monitoring agent to collect logs

Question 4

A company wants to monitor the CPU utilization of their Compute Engine instances and automatically trigger scaling actions if utilization exceeds 80% for 5 minutes. Which service should they use?

Accepted Answer

Managed instance group autoscaler. Managed instance group (MIG) autoscaler is the correct service because it is designed to automatically adjust the number of Compute Engine instances based on configured utilization metrics. By setting a target CPU utilization of 80% over a 5-minute window, the autoscaler will add or remove instances to maintain that threshold, directly meeting the requirement for automatic scaling actions.

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Cloud Monitoring

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Cloud Scheduler

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Cloud Load Balancing

Question 5

An application uses Cloud SQL and is experiencing slow query performance. The team wants to monitor query latency and identify slow queries. Which Google Cloud tool should they use?

Accepted Answer

Cloud SQL Insights. Cloud SQL Insights is the correct tool because it is specifically designed to provide detailed query performance diagnostics for Cloud SQL databases. It captures query latency, execution plans, and wait events, enabling teams to identify and troubleshoot slow queries directly within the Cloud SQL console without additional configuration.

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Cloud Debugger

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Cloud Monitoring

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Cloud Trace

Question 6

A company has a multi-region deployment of their application on GKE. They need to monitor service-level indicators (SLIs) like availability and latency across regions. They want a single pane of glass to view SLO compliance. What should they use?

Accepted Answer

Cloud Monitoring SLO monitoring. Cloud Monitoring SLO monitoring is the correct choice because it provides a unified dashboard (single pane of glass) to define, track, and visualize service-level indicators (SLIs) such as availability and latency across multiple GKE regions. It allows you to set SLO targets, monitor compliance over time, and receive alerts when the error budget is depleted, all within a single monitoring view.

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Cloud Logging with log-based metrics

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Cloud Profiler cross-region profiles

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Cloud Trace multi-region traces

Question 7

A developer wants to profile their application's CPU and memory usage to identify performance bottlenecks. Which TWO Google Cloud services should they use?

Accepted Answer

Cloud Profiler. Cloud Profiler (Option C) is the correct service for profiling CPU and memory usage because it continuously gathers and analyzes call stacks and resource consumption across your application, identifying the functions that consume the most resources. This directly addresses the developer's goal of pinpointing performance bottlenecks in CPU and memory.

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Cloud Logging

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Cloud Debugger

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Cloud Trace

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Cloud Monitoring

Question 8

A team wants to monitor a web application's uptime from multiple locations. Which THREE Google Cloud monitoring features should they use?

Accepted Answer

Uptime checks. Uptime checks are the correct service for monitoring web application availability from multiple locations. They allow you to configure HTTP, HTTPS, or TCP checks from Google Cloud's global vantage points, verifying that the application responds correctly and within specified timeouts. This directly addresses the requirement to monitor uptime from multiple geographic locations.

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Log-based metrics

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Error Reporting

Question 9

A company's application on GKE is experiencing performance degradation. They want to use Google Cloud operations tools to identify the root cause. Which THREE tools should they use in combination?

Accepted Answer

Cloud Trace. Cloud Trace is correct because it provides distributed tracing capabilities that allow you to analyze latency across microservices in a GKE application. By collecting trace data from each request as it propagates through services, Cloud Trace helps identify performance bottlenecks, such as slow downstream calls or inefficient database queries, which are common causes of performance degradation.

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Cloud Profiler

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Cloud Debugger

Question 10

The alert is not firing even though error_count metric occasionally spikes above 10. What is the most likely reason?

Accepted Answer

The duration of 300s means the condition must remain >10 for 5 minutes, so brief spikes do not trigger.. Option C is correct because the alert condition is configured with a duration of 300 seconds (5 minutes), meaning the error_count metric must remain above 10 for the entire 5-minute window before the alert fires. Brief, transient spikes that exceed 10 but do not persist for the full duration will not trigger the alert, which is the most likely reason the alert is not firing despite occasional spikes.

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The aggregations are incorrect; should use REDUCE_MAX.

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The filter specifies gke_container but the metric might be from other resources.

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The comparison should be COMPARISON_GT_OR_NAN.

Question 11

What conclusion can be drawn from these traces?

Accepted Answer

The processPayment span is the bottleneck.. The trace shows that the 'processPayment' span has the longest duration (300ms) compared to the other spans, indicating it is the primary contributor to the overall latency. In distributed tracing, the span with the highest execution time is typically the bottleneck, as it delays the completion of the entire request. The other spans complete quickly, so optimizing 'processPayment' would yield the greatest performance improvement.

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The overall request latency is 300ms.

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The sendConfirmation span is failing.

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The validateCart span has errors.

Question 12

What is the first step to resolve this error?

Accepted Answer

Add a null check on line 45.. Option D is correct because the error is a NullReferenceException, which occurs when code attempts to access a member of a null object. Adding a null check on line 45 prevents the exception by ensuring the object is not null before use, which is the standard first step in debugging such runtime errors in managed code environments like .NET or Java.

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Roll back the deployment.

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Restart the service.

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Increase memory for the service.

Question 13

A web application hosted on Compute Engine is experiencing slow response times during peak hours. Which Cloud Monitoring metric should be examined first to identify the bottleneck?

Accepted Answer

95th percentile request latency measured by Cloud Load Balancing. The 95th percentile request latency measured by Cloud Load Balancing is the most direct indicator of user-perceived performance degradation. High latency at the load balancer level captures the end-to-end response time, including network, backend processing, and queuing delays, making it the first metric to examine when diagnosing slow response times during peak hours.

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CPU utilization of backend instances

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Number of incoming requests per second

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Memory usage of backend instances

Question 14

A developer deploying a new version of a microservice sees a sudden increase in error logs in Cloud Logging. The errors are 500 responses from the service. What is the most efficient way to investigate the root cause?

Accepted Answer

Use Cloud Trace to view the trace of failed requests. Cloud Trace provides end-to-end latency data and can capture detailed spans for individual requests, including those that resulted in 500 errors. By filtering traces to failed requests, you can pinpoint the exact service or function call that caused the error, making it the most efficient root-cause investigation method without requiring code changes or additional instrumentation.

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Revert to the previous version immediately

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Check the CPU and memory metrics in Cloud Monitoring

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Analyze the error logs using Log Analytics and create a log-based metric

Question 15

A company wants to create an SLO for their API with a target of 99.9% availability over a 30-day rolling window. They are using Cloud Monitoring. Which combination of resources and techniques should they use?

Accepted Answer

Create an uptime check and a log-based metric for errors. Use the SLI formula: (successful requests / total requests).. Option C is correct because it combines an uptime check (to measure total requests) with a log-based metric for errors (to count failed requests), allowing the SLI formula (successful requests / total requests) to compute availability. This approach directly aligns with the 99.9% availability target over a 30-day rolling window, using Cloud Monitoring's native capabilities without external tools or irrelevant latency metrics.

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Manually compute availability using external monitoring tools.

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Use the Cloud Monitoring SLO service with a request latency SLI.

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Use Cloud Trace to measure latency and create a custom metric.