PCDOE Implementing service monitoring strategies Practice Test 4 — 15 Questions

Question 1

Your team is implementing SLO monitoring. Which TWO tools should they use to create and monitor SLIs?

Accepted Answer

Cloud Monitoring. Cloud Monitoring (option B) is the correct tool for creating and monitoring Service Level Indicators (SLIs) because it allows you to define custom metrics, set up alerting policies, and create dashboards to track the performance of your services. Cloud Logging (option C) is also correct because SLIs often rely on log-based metrics, such as request latency or error counts, which are extracted from log entries using log-based metrics in Cloud Logging. Together, these two tools provide the data ingestion and monitoring capabilities needed to define and observe SLIs.

Answer

Error Reporting

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Cloud Trace

Answer

Cloud Profiler

Question 2

You are designing a monitoring strategy for a cloud-native application. Which THREE components are essential for observability?

Accepted Answer

Metrics. Metrics (A) are essential because they provide quantitative, time-series data about system health and performance, such as CPU utilization, request latency, and error rates. In cloud-native observability, metrics are typically collected via Prometheus or similar systems, enabling trend analysis and threshold-based alerting. Without metrics, you cannot measure the overall state of your application or infrastructure over time.

Answer

Alerts

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Dashboards

Question 3

A company needs to monitor custom application metrics from Compute Engine instances. Which TWO methods can be used?

Accepted Answer

Install Cloud Monitoring agent on instances. The Cloud Monitoring agent is specifically designed to collect custom application metrics from Compute Engine instances and send them to Cloud Monitoring. It supports both third-party applications and custom metrics via its built-in integration with collectd and a configuration interface for defining custom metrics. This is the standard, supported method for monitoring custom metrics from VMs.

Answer

Use the deprecated Stackdriver Agent

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Use Cloud Trace

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Install Cloud Logging agent

Question 4

Based on the exhibit, which Cloud Logging query filter will return all logs of this type?

Accepted Answer

severity=ERROR. Cloud Logging uses the `severity=ERROR` syntax to filter logs by exact severity level. The `=` operator performs an exact match on the severity field, which is a standard LogEntry field with predefined values (DEFAULT, DEBUG, INFO, NOTICE, WARNING, ERROR, CRITICAL, ALERT, EMERGENCY). This filter returns all logs where the severity is exactly ERROR, matching the requirement.

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severity>=ERROR

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severity:"ERROR"

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jsonPayload.severity:ERROR

Question 5

Refer to the exhibit. What is the effect of the metricRelabelings section in this ServiceMonitor?

Accepted Answer

It drops all metrics whose name starts with 'container_'.. The `metricRelabelings` section in a ServiceMonitor uses Prometheus relabeling rules to modify or filter metrics before ingestion. The `action: drop` directive, combined with a `regex: 'container_.*'` pattern matching the metric name, causes all metrics whose names start with 'container_' to be dropped (i.e., not scraped). This is a common pattern to exclude unwanted metrics and reduce cardinality.

Answer

It adds a label called 'container_' to all metrics.

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It only keeps metrics whose name starts with 'container_'.

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It renames all metrics with the prefix 'container_' to remove the prefix.

Question 6

A team wants to monitor a Google Cloud Run service for application crashes. Which Google Cloud tool automatically captures and notifies on application errors?

Accepted Answer

Error Reporting. Error Reporting (D) is the correct answer because it is a Google Cloud service specifically designed to automatically capture, aggregate, and notify on application errors, including crashes in Cloud Run services. It ingests error events from Cloud Logging and provides real-time alerts and dashboards, making it the dedicated tool for this use case.

Answer

Cloud Logging

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Cloud Monitoring

Answer

Cloud Console

Question 7

An SRE team needs to define an SLI for a web service's availability SLO of 99.9%. Which metric should they use?

Accepted Answer

Uptime check success rate. An uptime check success rate directly measures the proportion of time the service is reachable and responding, which aligns with the definition of availability for a 99.9% SLO. This metric is typically derived from synthetic probes or health check endpoints (e.g., HTTP 200 responses) and reflects the binary state of the service being up or down, making it the appropriate SLI for availability.

Answer

Error budget

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CPU utilization

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Request latency (p99)

Question 8

A company runs a microservices architecture on GKE with Istio. They want to generate custom request-level metrics for SLO tracking without modifying application code. Which approach is most efficient?

Accepted Answer

Set up Istio telemetry with the Cloud Monitoring adapter. Istio's telemetry v2 can be configured to export custom request-level metrics (e.g., latency, error rate) to Cloud Monitoring via the Cloud Monitoring adapter without modifying application code. This approach leverages Istio's sidecar proxy to capture metrics at the service mesh layer, making it the most efficient and non-invasive method for SLO tracking.

Answer

Write a custom Prometheus exporter deploying in each pod

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Use the Cloud Monitoring agent to scrape metrics from pods

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Enable Stackdriver for GKE (deprecated)

Question 9

A developer wants to view logs from all pods in a GKE namespace in real time. Which command-line tool should they use?

Accepted Answer

gcloud logging tail. The `gcloud logging tail` command streams logs in real time from all pods in a GKE namespace, as it directly queries the Cloud Logging API for live log entries. This is the correct tool for real-time log streaming across multiple pods, unlike `kubectl logs` which only shows logs from a single pod or a limited set. The command supports filtering by resource labels, such as `--filter="resource.labels.namespace_name=NAMESPACE"`, to scope the output to a specific namespace.

Answer

gcloud logging read

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Cloud Console Logs Viewer

Answer

kubectl logs --tail=100

Question 10

A DevOps engineer needs to verify if a load balancer's health check is behaving normally by examining historical trends. Where should they look?

Accepted Answer

Cloud Monitoring Metrics Explorer. Cloud Monitoring Metrics Explorer is the correct place to examine historical trends of a load balancer's health check because it provides time-series metrics such as `https/backend_request_count`, `https/backend_latencies`, and health check probe success/failure counts. These metrics can be queried over custom time ranges and aggregated to detect anomalies or degradation patterns, which is exactly what the question asks for — verifying historical trends of health check behavior.

Answer

Cloud Logging

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Cloud Console health check page

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Cloud Load Balancing logs

Question 11

You need to monitor a multi-step login flow that involves calling an API, validating a token, and redirecting. Which type of uptime check should you use?

Accepted Answer

Synthetic Monitor (Cloud Functions). A synthetic monitor using Cloud Functions is the correct choice because it can simulate a multi-step login flow by executing custom code that calls an API, validates a token, and performs a redirect. Unlike simple HTTP GET or TCP checks, synthetic monitors can handle stateful interactions and conditional logic, making them ideal for complex transaction monitoring.

Answer

Cloud Endpoint check

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TCP check

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HTTP GET check

Question 12

Which Google Cloud tool automatically captures and visualizes traces for applications running on App Engine?

Accepted Answer

Cloud Trace. Cloud Trace is the correct Google Cloud tool for automatically capturing and visualizing latency traces from applications running on App Engine. It provides end-to-end latency insights by collecting trace data from distributed systems, enabling you to analyze request performance and identify bottlenecks without manual instrumentation.

Answer

Cloud Debugger

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Cloud Monitoring

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Cloud Logging

Question 13

An organization wants to be alerted when the total size of a Cloud Storage bucket exceeds 1 TB. Which metric should they monitor?

Accepted Answer

storage.googleapis.com/storage/total_bytes. The metric `storage.googleapis.com/storage/total_bytes` directly measures the total amount of data stored in a Cloud Storage bucket, including all object data and metadata. Monitoring this metric allows the organization to set an alert threshold at 1 TB (1,099,511,627,776 bytes) to trigger when the bucket exceeds that size. This is the correct metric for tracking storage capacity usage.

Answer

storage.googleapis.com/storage/object_count

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storage.googleapis.com/storage/network_sent_bytes

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storage.googleapis.com/storage/request_count

Question 14

A team uses Cloud Monitoring alerting policies with multiple conditions. They want an incident to fire only when both conditions are met simultaneously. What should they configure?

Accepted Answer

Set the alerting policy combiner to 'AND'. Cloud Monitoring alerting policies support a 'combiner' field that can be set to 'AND' to require that all conditions are met simultaneously before the incident fires. This ensures the alert triggers only when both conditions are true at the same evaluation window, rather than when either condition is met.

Answer

Create two separate alerting policies

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Create a single condition with a ratio metric

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Use a log-based metric condition

Question 15

Which TWO options are valid methods to create a custom metric descriptor in Cloud Monitoring?

Accepted Answer

Using the gcloud commands: gcloud monitoring metrics create. The `gcloud monitoring metrics create` command directly invokes the Cloud Monitoring API to create a custom metric descriptor. Option E is correct because the Cloud Monitoring API's `CreateMetricDescriptor` method is the programmatic way to define a custom metric, specifying its type, unit, and labels. Both methods result in a new metric descriptor being registered in the project's metric store.

Answer

Creating a log-based metric

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Using Terraform resource google_monitoring_metric_descriptor

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Deploying a Prometheus exporter with the Ops Agent