PCD Managing application performance monitoring Practice Test 3 — 15 Questions

Question 1

A team is using Cloud Monitoring to set up an alerting policy for a Compute Engine instance that runs a web server. The team wants to be notified if the instance's CPU utilization exceeds 80% for 5 minutes. Which threshold type should they use?

Accepted Answer

Metric threshold. Option B is correct because a metric threshold alerting policy directly monitors a numeric metric (e.g., CPU utilization) and triggers when the value exceeds a defined threshold (80%) for a specified duration (5 minutes). This is the standard approach for simple threshold-based alerts on a single metric in Cloud Monitoring.

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Ratio threshold

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MQL (Monitoring Query Language)

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Forecast threshold

Question 2

An application deployed on Google Kubernetes Engine is experiencing intermittent latency spikes. The team has enabled Cloud Trace and sees that a specific gRPC call to a backend service occasionally takes >500ms. However, the backend service's logs show no errors. What is the most likely cause that the team should investigate further?

Accepted Answer

The gRPC client is not using connection pooling, causing frequent TLS handshakes.. Option B is correct because gRPC relies on HTTP/2, which multiplexes multiple requests over a single persistent connection. If the client does not reuse connections, each new request triggers a new TLS handshake, which can add significant latency (often 100-500ms) due to certificate exchange and cryptographic operations. This intermittent behavior occurs when the client creates a new channel per request rather than reusing a connection pool, leading to sporadic high-latency calls without backend errors.

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The Cloud Trace sampling rate is too low, causing statistical noise.

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The backend service is under-provisioned and experiencing resource contention only during peak traffic.

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The network latency between the client and backend is high due to a misconfigured VPC firewall.

Question 3

A company runs a microservices architecture on Cloud Run. They want to measure the error budget for a critical service using a custom SLI based on the ratio of successful requests (HTTP 200-499) to total requests. They have set an SLO of 99.9% over a 30-day window. Which Cloud Monitoring feature should they use to track this?

Accepted Answer

Service Level Objectives (SLOs) with a custom SLI metric.. Option A is correct because Cloud Monitoring's Service Level Objectives (SLOs) feature natively supports custom SLI metrics, allowing you to define a ratio of successful requests (HTTP 200-499) to total requests as a custom SLI. This directly enables tracking the error budget against the 99.9% SLO over a 30-day window, without needing to build external dashboards or rely on latency or uptime checks.

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Cloud Trace spans to calculate latency-based SLIs.

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Uptime checks with a custom status code classifier.

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Log-based metrics to count requests and errors, then create a custom dashboard.

Question 4

A developer needs to view detailed performance profiles of a Java application running on Compute Engine to identify CPU hotspots. Which Google Cloud service should they use?

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct service because it provides continuous, low-overhead CPU and heap profiling for Java applications running on Compute Engine. It uses statistical sampling to identify which methods consume the most CPU time, enabling developers to pinpoint hotspots without requiring code changes or redeployment.

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Cloud Monitoring

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Cloud Trace

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Cloud Logging

Question 5

An operations team has set up a Cloud Monitoring alerting policy that fires when the 99th percentile latency of a service exceeds 200ms for 1 minute. They notice that the alert fires frequently during normal traffic patterns. What is the most likely issue with the alert configuration?

Accepted Answer

The alerting policy uses a rolling window that is too short for the metric's natural variation.. The alert fires frequently during normal traffic because the 1-minute rolling window is too short to smooth out natural latency spikes. A short window captures transient variations as if they were sustained, causing false positives. Lengthening the window (e.g., to 5 minutes) would reduce noise and better reflect true performance trends.

Answer

The metric's alignment period is set to align by rate instead of mean.

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The service is actually experiencing high latency, and the threshold should be lowered.

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The alerting policy is using a forecast threshold instead of a metric threshold.

Question 6

A company uses Cloud Monitoring with custom metrics. They have a custom metric called 'requests_total' with labels 'endpoint', 'status_code'. They want to create an alert that fires if the error rate (status_code >=500) for any endpoint exceeds 5% over a 5-minute window. Which MQL query should they use?

Accepted Answer

fetch custom::requests_total | { filter status_code >= 500 ; group_by [endpoint], sum() } / { group_by [endpoint], sum() } | condition gt 0.05. Option A is correct because it first filters for error responses (status_code >= 500), then groups by endpoint and sums the error count, and divides that by the total count per endpoint (also grouped and summed). This computes the error rate per endpoint, and the condition fires when that rate exceeds 0.05 (5%) over the 5-minute window. The use of two separate group_by operations within a join (the `{ ... } / { ... }` syntax) is the correct MQL pattern for calculating a ratio per label.

Answer

fetch custom::requests_total | filter status_code < 500 | ratio | condition gt 0.05

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fetch custom::requests_total | group_by [endpoint], sum() | filter status_code >= 500 | ratio | condition gt 0.05

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fetch custom::requests_total | filter status_code >= 500 | ratio | condition gt 0.05

Question 7

A team wants to monitor the availability of an external API by pinging it every minute from multiple locations around the world. Which Cloud Monitoring feature should they use?

Accepted Answer

Uptime checks. Uptime checks are the correct Cloud Monitoring feature because they are specifically designed to verify the availability of external services by sending HTTP, HTTPS, or TCP requests from multiple locations around the world. This matches the requirement to ping an external API every minute from multiple global locations, providing detailed latency and status data without custom scripting.

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Synthetic monitoring

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Custom metrics from a cron job

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Log-based metrics

Question 8

A developer is using Cloud Logging and wants to export logs from a specific project to BigQuery for long-term analysis. They have created a log sink and given the appropriate permissions, but logs are not appearing in BigQuery. What is the most likely cause?

Accepted Answer

The service account used for the sink does not have the 'bigquery.dataEditor' role.. Option D is correct because the log sink uses a service account to write logs to BigQuery. Even if the sink is configured correctly, the service account must have the 'bigquery.dataEditor' role on the destination dataset to insert log entries. Without this role, the sink will fail silently, and logs will not appear in BigQuery.

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The sink's filter is too restrictive and no logs match.

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The sink's destination BigQuery dataset is in a different region than the logs.

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The log entries are not in JSON format.

Question 9

A company has a Cloud Run service that uses Cloud SQL. They notice that the number of database connections is increasing over time, causing connection pool exhaustion. They have enabled Cloud Monitoring and see a custom metric for active DB connections. To proactively alert when the connection count exceeds 80% of the maximum pool size (which is 100), which alerting approach is most efficient?

Accepted Answer

Create a ratio alert using an MQL query that divides the active connections by the max connections and alerts when > 0.8.. Option D is correct because it creates a ratio alert using MQL to divide the active connections by the maximum pool size (100), triggering when the ratio exceeds 0.8 (80%). This directly measures the utilization of the connection pool, which is the most efficient way to alert on impending exhaustion. It avoids hardcoding a static threshold that would break if the pool size changes, and it uses the custom metric already being monitored.

Answer

Create a metric threshold alert on the custom metric with condition > 80.

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Create a forecast alert to predict when connections will exceed 80.

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Create an alert on the Cloud SQL system metric for 'cloudsql.googleapis.com/database/connections/num_failed_reserved'.

Question 10

A DevOps team is migrating an on-premises monitoring solution to Google Cloud. They need to collect custom application metrics from a batch processing job running on Compute Engine. Which two services can ingest custom metrics into Cloud Monitoring? (Choose two.)

Accepted Answer

Cloud Logging with log-based metrics. Option C is correct because Cloud Logging can ingest any log entry, and log-based metrics allow you to extract numeric values from log content to create custom metrics that appear in Cloud Monitoring. Option E is correct because the Cloud Monitoring API lets you define custom metric descriptors and then write time-series data directly to those metrics, bypassing any agent or log pipeline.

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Cloud Profiler API

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Stackdriver Monitoring agent with custom plugin

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Cloud Trace API

Question 11

A company is using Cloud Monitoring to set up an SLO for a latency-sensitive API. They have defined a custom SLI: the proportion of requests with latency under 200ms. Which three components must they define to create a complete SLO configuration? (Choose three.)

Accepted Answer

A target (e.g., 99.9%). Option A is correct because a target (e.g., 99.9%) defines the desired proportion of good events over a compliance window, which is essential for an SLO. In Cloud Monitoring, the target is the threshold against which the SLI is measured to determine if the SLO is met.

Answer

A burn rate alert policy

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A metric threshold alert

Question 12

A developer wants to view real-time logs from a running application on Compute Engine. Which two methods can they use to stream logs? (Choose two.)

Accepted Answer

Using the Logs Explorer's 'Stream logs' feature. Option A is correct because the Logs Explorer in the Google Cloud Console provides a 'Stream logs' feature that allows you to view real-time log entries as they are ingested by Cloud Logging. This is ideal for monitoring a running Compute Engine instance without needing to SSH into it. Option C is correct because the `gcloud logging tail` command streams log entries from Cloud Logging in real time, using the Logging API's tail method, and can filter by resource type (e.g., `gce_instance`) or log name.

Answer

Using gcloud compute ssh and running journalctl -f

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Using Cloud Monitoring's metrics explorer

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Using gcloud app logs tail

Question 13

Refer to the exhibit. A team is using Cloud Monitoring with MQL to alert on CPU utilization per zone. They notice that the alert fires even when no single instance in a zone has CPU>80%, because the average across instances in the zone exceeds 80%. What change should they make to the MQL query to alert only when any individual instance exceeds 80%?

Accepted Answer

Change the group_by to group_by [instance_id] and remove zone grouping.. Option D is correct because the current MQL query uses `group_by [zone]` to compute the mean CPU utilization per zone, which averages all instances in a zone together. By changing the grouping to `group_by [instance_id]` and removing the zone grouping, the alert will evaluate each instance individually, firing only when a single instance's CPU exceeds 80%, rather than when the zone-wide average exceeds the threshold.

Answer

Remove the group_by and use a filter instead.

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Add a filter for each instance individually.

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Use a ratio instead of mean.

Question 14

Refer to the exhibit. A developer sees this log entry in Cloud Logging. The application is running on Compute Engine. Which tool should they use to further diagnose the cause of the connection refusal?

Accepted Answer

VPC Flow Logs to analyze network traffic.. The log entry indicates a connection refusal, which is a network-level issue. VPC Flow Logs capture metadata about network traffic to and from Compute Engine instances, including whether connections were accepted or rejected. By analyzing these logs, the developer can identify the source and destination IPs, ports, and protocol, and determine if a firewall rule or routing issue is causing the refusal.

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Cloud Monitoring to check network metrics.

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Cloud Profiler to identify CPU bottlenecks.

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Cloud Trace to trace the request flow.

Question 15

Refer to the exhibit. A team has created this alerting policy for a Cloud Run service. However, the alert never fires even though the error rate sometimes exceeds 1%. What is the most likely issue?

Accepted Answer

The filter is using response_code_class > 500, missing 500 errors.. Option C is correct because the filter `response_code_class > 500` uses a strict greater-than operator, which excludes HTTP 500 errors (the class value is exactly 500). In Cloud Monitoring, `response_code_class` for a 500 error is 500, so the condition `> 500` only matches classes like 501, 502, etc., missing the most common server errors. To capture all 5xx errors, the filter should be `response_code_class >= 500` or `response_code_class = 500`.

Answer

The threshold is set via conditionMonitoringQuery but thresholdValue is null, causing conflict.

Answer

The group_by [] aggregates across all revisions, but errors might be per revision.

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The duration is 0s, so the alert should fire immediately; it's not causing the issue.