PCD Managing application performance monitoring Practice Test 5 — 15 Questions

Question 1

A team wants to monitor CPU utilization on their Compute Engine instances. They need an alert that sends a notification when the average CPU utilization across all instances in a project exceeds 80% for more than 5 minutes. Which alerting configuration should they use?

Accepted Answer

Create an alert policy with a metric threshold condition for compute.googleapis.com/instance/cpu/utilization, aggregated across all instances with alignment period 1 min and duration 5 min. Option D is correct because Cloud Monitoring alert policies allow you to define a metric threshold condition using the `compute.googleapis.com/instance/cpu/utilization` metric, aggregate it across all instances in the project, and set an alignment period of 1 minute with a duration of 5 minutes. This configuration ensures the alert fires only when the average CPU utilization exceeds 80% for a sustained period of 5 minutes, meeting the exact requirement.

Answer

Use Cloud Scheduler to periodically check CPU and trigger notification

Answer

Create a log-based alert using metrics from Cloud Logging

Answer

Use an uptime check to monitor CPU utilization

Question 2

A company uses Cloud Logging to store application logs. They need to keep logs for 3 years for compliance. What is the most cost-effective way to store logs for this duration?

Accepted Answer

Create a sink to export logs to Cloud Storage with object lifecycle rules. Cloud Logging's default retention is limited (e.g., 30 days for logs, with some exceptions up to 400 days), so it cannot meet a 3-year compliance requirement. Exporting logs to Cloud Storage and applying object lifecycle rules allows you to automatically transition objects to lower-cost storage classes (e.g., from Standard to Nearline, Coldline, or Archive) and delete them after the retention period, minimizing cost while meeting the 3-year retention need.

Answer

Use Cloud Logging's default retention

Answer

Create a sink to export logs to Pub/Sub

Answer

Create a sink to export logs to BigQuery

Question 3

A team is using Cloud Trace to analyze performance of a microservices application. They notice that some spans are missing from the trace. What is the most likely cause?

Accepted Answer

The trace sampling rate is set too low. The most likely cause of missing spans in Cloud Trace is that the trace sampling rate is set too low. Cloud Trace uses a configurable sampling rate to control how many requests are traced; if the rate is low, many requests are not sampled, resulting in incomplete traces. This is a common configuration issue, not a failure to send traces or a network problem.

Answer

The application is not sending traces for all services

Answer

There is a network latency issue

Answer

The Cloud Trace API is disabled for some projects

Question 4

You need to create an uptime check for an external HTTPS endpoint and configure an alert that sends a notification if the check fails for 3 consecutive attempts. Which configuration is correct?

Accepted Answer

Create an uptime check with check interval 1 min and alert condition with duration 3 min. Option B is correct because to trigger an alert after 3 consecutive failures with a 1-minute check interval, the alert condition must have a duration of 3 minutes. This ensures that the alert fires only when the endpoint has been down for three successive checks, matching the requirement exactly.

Answer

Create an uptime check with check interval 5 min and alert condition with duration 3 min

Answer

Create an uptime check with check interval 5 min and alert condition with downtime 15 min

Answer

Create an uptime check with check interval 1 min and alert condition with duration 1 min

Question 5

A developer wants to view real-time latency metrics for their App Engine application. Where can they find this data?

Accepted Answer

Cloud Monitoring - Metrics Explorer. Cloud Monitoring's Metrics Explorer allows you to view real-time latency metrics for App Engine by querying the `appengine.googleapis.com/http/server/response_latencies` metric. This provides a time-series view of request latency distributions, enabling immediate observation of performance changes as they occur.

Answer

Cloud Profiler

Answer

Cloud Logging

Answer

Cloud Trace

Question 6

A company is using Cloud Monitoring to track custom metrics published from an on-premises application using the Monitoring API. The metrics are published every 30 seconds. The team wants to create an alert that fires if the metric goes below a threshold for more than 1 minute. Which alert condition type should they use?

Accepted Answer

Metric type: custom.googleapis.com, condition: metric threshold (below value), duration: 60s. Option D is correct because the requirement is to fire an alert when the metric value goes below a threshold for more than 1 minute. The 'metric threshold (below value)' condition type directly monitors a metric against a lower bound and evaluates the condition over the specified duration (60s) before triggering. This aligns with the need to detect sustained low values, not just a single data point.

Answer

Metric type: custom.googleapis.com, condition: metric lower bound, duration: 60s

Answer

Metric type: custom.googleapis.com, condition: metric change, duration: 60s

Answer

Metric type: custom.googleapis.com, condition: metric absence, duration: 60s

Question 7

Which TWO of the following are valid ways to export Cloud Logging logs to BigQuery?

Accepted Answer

Use a Dataflow pipeline to stream logs from Pub/Sub to BigQuery. Option B is correct because you can use a Dataflow pipeline to read Cloud Logging logs from a Pub/Sub topic (where logs are routed via a log sink) and stream them into BigQuery for real-time analysis. This is a common pattern for custom log processing and transformation before loading into BigQuery. Option C is correct because Cloud Logging allows you to create a log sink directly with a destination of a BigQuery dataset, which automatically exports logs in near real-time without additional infrastructure.

Answer

Use the Logging API to write logs directly to BigQuery

Answer

Use the BigQuery Data Transfer Service for Cloud Logging

Answer

Use Cloud Monitoring to send logs to BigQuery

Question 8

Which THREE metrics are commonly used to create a Service Level Indicator (SLI) for availability of an HTTP-based service?

Accepted Answer

Uptime check success rate. Uptime check success rate directly measures whether the service is reachable and responding, typically via periodic HTTP probes (e.g., GET /health). A successful response (e.g., HTTP 200) indicates availability, while failures (timeouts, connection errors) indicate unavailability. This is a standard SLI for availability in HTTP-based services.

Answer

CPU utilization

Answer

Request count

Question 9

Which TWO statements about Cloud Trace are correct?

Accepted Answer

Trace can be integrated with Cloud Monitoring for alerting. Option A is correct because Cloud Trace can be integrated with Cloud Monitoring to create alerting policies based on trace data, such as latency thresholds or error rates. This integration allows you to set up notifications when specific trace conditions are met, enabling proactive performance monitoring.

Answer

Trace collects latency data from all requests by default

Answer

Trace automatically creates dashboards for visualization

Answer

Trace supports auto-scaling based on latency

Question 10

Refer to the exhibit. You are reviewing a Cloud Monitoring MQL query. What is the purpose of this query?

Accepted Answer

It filters for instances with CPU utilization > 90% and then computes the 5-minute average.. Option D is correct because the MQL query uses the `filter` clause to first select only time series where `cpu.utilization` exceeds 90%, and then applies the `avg` aggregation over a 5-minute window. This order of operations ensures that the average is computed only on the filtered data points, not on all instances.

Answer

It displays the raw CPU utilization data points that exceed 90%.

Answer

It shows the 5-minute average CPU utilization for all instances, then filters out those with average > 90%.

Answer

It computes the 5-minute average of CPU utilization and then selects instances where any data point exceeded 90%.

Question 11

Refer to the exhibit. You are analyzing application logs and notice that some logs contain a 'trace' field. What does this field enable?

Accepted Answer

It links the log entry to a specific Cloud Trace trace for end-to-end latency analysis. The 'trace' field in a log entry contains the trace ID and span ID that link the log to a specific Cloud Trace trace. This enables end-to-end latency analysis by correlating log entries with the distributed trace that generated them, allowing you to see the full request path across services.

Answer

It is used by Cloud Monitoring to correlate logs with metrics

Answer

It is used to export the log to Cloud Trace

Answer

It indicates that the log was generated by the Cloud Trace agent

Question 12

Refer to the exhibit. The alert fires when what happens?

Accepted Answer

When the rate of responses on App Engine exceeds 10 per second for 5 minutes. The alert is configured to fire when the rate of responses on App Engine exceeds 10 per second for a sustained period of 5 minutes. This is a rate-based threshold, not a cumulative count or latency metric, which is why option A correctly describes the condition.

Answer

When the cumulative response count on App Engine exceeds 10 for 5 minutes

Answer

When the latency exceeds 10 seconds for 5 minutes

Answer

When the response rate drops below 10 per second for 5 minutes

Question 13

A company is deploying a microservices architecture on Google Kubernetes Engine (GKE). They need to monitor inter-service latency and error rates. Which set of Google Cloud services should they use to collect and visualize these metrics?

Accepted Answer

Cloud Monitoring, Cloud Logging, and Cloud Trace. Option B is correct because Cloud Monitoring collects metrics like latency and error rates, Cloud Logging aggregates logs for deeper analysis, and Cloud Trace provides distributed tracing to track requests across microservices. Together, they enable end-to-end observability of inter-service performance in GKE, with Cloud Monitoring visualizing the data via dashboards and alerts.

Answer

Cloud Trace, Cloud Debugger, and Cloud Profiler

Answer

Cloud Logging, Cloud Run, and Cloud Build

Answer

Cloud Monitoring, Cloud Functions, and Cloud Pub/Sub

Question 14

A Cloud Run service is experiencing intermittent high latency. The team has enabled Cloud Trace. They want to identify the root cause by analyzing traces. What should they look for in the Trace viewer?

Accepted Answer

Spans with high latency and error status. In Cloud Trace, the root cause of intermittent high latency is identified by examining spans—the fundamental units representing work in a distributed system. Spans with high latency directly indicate where time is being spent, and an error status (e.g., HTTP 5xx or gRPC error codes) pinpoints a failure that could be causing retries or blocking, leading to the observed latency. This combination is the most direct signal for root cause analysis in trace data.

Answer

High container CPU usage

Answer

Large number of concurrent requests

Answer

Frequent log entries with 'WARNING'

Question 15

An organization wants to create custom metrics based on application logs to track business KPIs. They need to ensure these metrics are available for alerting within minutes. Which approach should they use?

Accepted Answer

Write logs to Cloud Logging and use log-based metrics. Log-based metrics in Cloud Logging allow you to define custom metrics from log entries using filters or regular expressions. These metrics are ingested and available for alerting within minutes because they are processed in near real-time by the Cloud Monitoring backend, without requiring external data pipelines or custom code.

Answer

Export logs to BigQuery and use scheduled queries

Answer

Use OpenTelemetry collector to send metrics directly

Answer

Use Cloud Functions to parse logs and push custom metrics via API