CCNA Pcde Monitoring Strategies Questions — Page 1 of 2

Multi-Selecthard

You are setting up structured logging for an application running on Compute Engine. Your logs should include trace context and severity. Which TWO fields should you include in the JSON payload to enable correlation with Cloud Trace? (Select 2)

Select 2 answers

A.httpRequest

B.message

C.spanId

D.severity

E.trace

AnswersC, E

The spanId identifies the specific span.

Why this answer

To correlate logs with traces, you include the trace field (trace ID) and optionally spanId. The severity field is for log level, not correlation.

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MCQmedium

You want to use OpenTelemetry Collector to send traces from an on-premises application to Cloud Trace. Which exporter should you configure in the Collector pipeline?

A.logging

B.prometheus

C.otlp

D.googlecloud

AnswerD

Correct. The googlecloud exporter sends traces and metrics to Google Cloud operations suite.

Why this answer

The OpenTelemetry Collector has an exporter 'googlecloud' (or 'googlecloudtrace') that sends traces to Cloud Trace. The 'logging' exporter writes to stdout, 'otlp' sends to an OTLP receiver, and 'prometheus' exports metrics. For Cloud Trace, the googlecloud exporter is correct.

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MCQhard

An application running on GKE emits custom metrics via OpenTelemetry. The metrics need to be ingested into Cloud Monitoring and visualized in a dashboard. Which approach should the engineer use?

A.Install the OTel Collector as a DaemonSet and configure the Google Cloud Monitoring exporter.

B.Use the Cloud Monitoring API to directly ingest metrics from the application.

C.Configure the application to write logs in JSON format and create a log-based metric.

D.Use the Operations Agent on the node to scrape metrics from the application.

AnswerA

The OTel Collector can receive metrics and export them to Cloud Monitoring via the Google Cloud exporter.

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Multi-Selectmedium

Your team wants to collect custom metrics from a .NET application running on Compute Engine and send them to Cloud Monitoring. Which TWO approaches are valid?

Select 2 answers

A.Configure the application to emit metrics to a StatsD daemon and configure Cloud Monitoring to scrape it

B.Use the Google.Cloud.Monitoring.V3 client library to call the metric API directly

C.Install the Stackdriver Monitoring agent and enable custom metrics

D.Use gcloud beta monitoring metrics create to define and write metrics from the application

E.Instrument the application with OpenTelemetry .NET SDK and configure the Google Cloud exporter

AnswersB, E

Correct. The client library allows programmatic creation and writing of custom metrics.

Why this answer

Valid approaches: (1) Use the OpenTelemetry .NET SDK to instrument the application and export metrics to Cloud Monitoring via the Google Cloud exporter. (2) Use the Google.Cloud.Monitoring.V3 client library to write custom metrics directly via the Monitoring API. The other options are invalid: Stackdriver client is deprecated, StatsD is not directly supported by Cloud Monitoring, and gcloud commands are for administration, not application-level metric writing.

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MCQeasy

A DevOps engineer wants to create a custom metric in Cloud Monitoring that represents the number of requests processed by a service, measured from the start of each request to its completion. The metric should increment by 1 for each request and must be queryable for the count in the last 24 hours. Which metric kind would best suit this requirement?

A.DELTA

B.COUNT

C.GAUGE

D.CUMULATIVE

AnswerD

CUMULATIVE metrics increase monotonically and allow querying the total count over any time range by using delta or rate calculations.

Why this answer

A CUMULATIVE metric measures a value that increases over time, such as a count of requests. It is appropriate when you want to know the total count up to a point in time, which can be queried for any time range (e.g., last 24 hours) by subtracting the start value from the end value. GAUGE represents a snapshot value that can go up and down, DELTA represents a rate per time interval but does not accumulate.

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MCQmedium

An engineer wants to automatically group similar error messages from an application and track trends, with the ability to link from an error to the corresponding log entries and trace. Which Google Cloud service should they use?

A.Error Reporting

B.Cloud Logging with log-based metrics

C.Cloud Trace

D.Cloud Monitoring with custom dashboards

AnswerA

Error Reporting automatically groups exceptions, shows trends, and links to logs/traces.

Why this answer

Error Reporting automatically groups errors, shows trends, and integrates with Cloud Logging and Cloud Trace for full context.

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Multi-Selectmedium

A team wants to use OpenTelemetry to collect metrics, traces, and logs from their applications running on GKE. They plan to use the OTel Collector. Which TWO components should they configure in the Collector pipeline? (Choose 2)

Select 2 answers

A.Configure a receiver (e.g., OTLP receiver) to ingest telemetry from applications

B.Configure a processor to transform data

C.Configure an exporter for Cloud Monitoring (metrics)

D.Configure an exporter for Cloud Logging (logs)

E.Configure a sampler to reduce trace data volume

AnswersA, C

The receiver is the entry point for telemetry data.

Why this answer

The OTel Collector pipeline consists of receivers (data ingestion), processors (optional), and exporters (data output). For this scenario, they need a receiver to accept data and exporters to send to Google Cloud.

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Multi-Selecthard

A global application uses Cloud Trace to collect distributed traces. The current sampling rate of 100% is causing high costs and storage usage. The team wants to reduce sampling while still capturing representative data. Which THREE strategies should they consider?

Select 3 answers

A.Set a probabilistic sampling rate (e.g., 10%) in the OpenTelemetry SDK

B.Disable automatic instrumentation and only use manual instrumentation with Cloud Trace API

C.Configure rate limiting based on request path, sampling only critical endpoints

D.Use tail-based sampling in the OpenTelemetry Collector, retaining traces with errors or high latency

E.Increase the sampling rate to 100% to ensure no traces are missed

AnswersA, C, D

Correct. Head-based probabilistic sampling reduces volume while maintaining a representative sample.

Why this answer

Strategies to reduce trace volume: (1) Implement head-based sampling with a fixed rate like 10% in the OpenTelemetry SDK. (2) Use tail-based sampling via the OpenTelemetry Collector to sample based on error status or latency. (3) Sample based on request path — prioritize high-value endpoints. Disabling auto-instrumentation stops all traces; increasing rate worsens the issue; using only Cloud Trace API is not a sampling strategy.

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Multi-Selecthard

An organization wants to monitor a batch processing job that runs daily on Compute Engine. They need to be alerted if the job fails to produce output within 24 hours. Which THREE components should they use? (Choose 3)

Select 3 answers

A.Create a log-based counter metric that increments upon job completion

B.Create a Cloud Monitoring dashboard to visualize the metric

C.Configure a notification channel (e.g., email) for the alert

D.Create a Cloud Monitoring alert with a metric-absent condition for the counter metric with duration 24h

E.Use Cloud Trace to trace the job execution

AnswersA, C, D

This metric provides the signal of job completion.

Why this answer

A log-based metric counting success messages, an alert with metric-absent condition, and a notification channel. The metric-absent condition fires when the metric stops reporting (i.e., no success log within 24h).

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Multi-Selecteasy

You need to create an alerting policy in Cloud Monitoring for a custom metric. Which THREE components must be defined? (Select 3)

Select 3 answers

A.Log sink

B.Conditions

C.Notification channels

D.Documentation

E.Dashboard

AnswersB, C, D

Conditions define the metric and threshold.

Why this answer

An alerting policy consists of conditions (which specify when to trigger), notification channels (how to notify), and documentation (optional but recommended). Duration is part of a condition.

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MCQmedium

You are using Cloud Profiler to identify performance bottlenecks. You notice a function that consumes significant CPU time. However, the profiler shows only a small percentage of samples in that function. What is the most likely cause?

A.You need to enable sampling for the specific function.

B.The profiler only profiles heap memory, not CPU.

C.The profiler is not configured to profile CPU usage.

D.The function is not hot enough to be sampled frequently due to the low overhead sampling rate of 0.5%.

AnswerD

The low sampling rate means only a fraction of CPU time is sampled, so a function might appear less frequently.

Why this answer

Cloud Profiler uses statistical sampling and has a low overhead of about 0.5%. If a function appears with a small percentage, it may be that the sampling rate is too low to capture it accurately, or the function is not the main bottleneck. However, a common reason is that the profiler's sampling rate is low to keep overhead minimal.

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MCQmedium

A company uses Cloud Logging and wants to receive real-time notifications when the number of 5xx errors exceeds 100 per minute. They need to set up an alerting policy. Which metric type and condition should they use?

A.Create a log-based counter metric and use a metric-absent condition

B.Create a log-based counter metric and use a metric-threshold condition with rate alignment

C.Use a Cloud Monitoring gauge metric with a custom value

D.Use a logs-based alert with a threshold on the log entry count

AnswerB

Counter metric provides the count; rate alignment over 1 minute gives per-minute rate; threshold condition fires when exceeded.

Why this answer

A log-based counter metric can count errors per minute. The alert condition should be 'metric threshold' with 'CUMULATIVE' metric and 'rate' alignment to detect spikes.

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Multi-Selectmedium

A company is designing a monitoring strategy for a microservices application on Google Kubernetes Engine. They need to capture traces, metrics, and logs using a vendor-neutral approach. Which TWO components should they consider? (Choose 2)

Select 2 answers

A.OpenTelemetry Collector as a DaemonSet

B.Cloud Logging agent

C.Stackdriver agents on each node

D.Cloud Trace automatic instrumentation

E.OpenTelemetry SDK for instrumentation

AnswersA, E

The OTel Collector can receive telemetry and export to backends like Cloud Monitoring.

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MCQhard

Your organization mandates that all application logs must be structured JSON and include a trace ID for correlation with Cloud Trace. Developers are using various logging libraries. Which approach ensures consistent structured logging across all services with minimal code changes?

A.Use a centralized logging agent (e.g., Fluentd) to parse unstructured logs and add trace IDs.

B.Require developers to manually format JSON logs and include trace IDs from environment variables.

C.Adopt OpenTelemetry SDK and use its logging API to produce structured logs with trace context.

D.Use Cloud Logging's automatic trace ID extraction from log entries.

AnswerC

OpenTelemetry provides a standard way to include trace IDs in logs without manual effort.

Why this answer

The OpenTelemetry SDK provides a unified way to instrument logging, metrics, and traces. By using the OTel logging SDK, developers can emit structured logs that include trace context automatically. This ensures consistency across polyglot environments without requiring custom code.

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MCQmedium

A DevOps team wants to create a custom metric in Cloud Monitoring that counts the number of failed login attempts per user per minute. This metric should be of type DELTA. What is the correct approach?

A.Use the Metrics Explorer to define a new metric with type DELTA

B.Create a log-based metric using the Logs Explorer with a filter for failed logins, which automatically creates a CUMULATIVE metric

C.Use the Cloud Console to manually add a metric entry, setting type to DELTA

D.Create a metric descriptor using gcloud alpha monitoring metrics create with --metric-kind=DELTA and --value-type=INT64

AnswerD

Correct. gcloud alpha monitoring metrics create allows creating custom metric descriptors with specified kind and type.

Why this answer

DELTA metrics represent the change in a value over a time interval. To create such a metric, you must use the monitoring API to define a metric descriptor with metricKind DELTA and valueType INT64. The metric is then written via the API.

OpenTelemetry can also be used, but the direct API call is the most straightforward. Cloud Monitoring does not support direct creation of DELTA metrics via the Metrics Explorer; CLI examples use custom metric descriptors.

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MCQmedium

Your team uses Cloud Monitoring to set up an alerting policy for high CPU utilization. You want the policy to trigger only if CPU usage exceeds 80% for at least 5 minutes. Which condition configuration should you use?

A.Set the alignment period to 300 seconds

B.Set the forecast horizon to 300 seconds

C.Set the duration field to 300 seconds

D.Set the retest window to 300 seconds

AnswerC

Duration specifies how long the metric must be above the threshold before the condition is met.

Why this answer

The 'duration' field in a metric threshold condition specifies how long the metric must violate the threshold before triggering. Setting duration to 300 seconds (5 minutes) achieves the requirement. Alignment period and retest window are separate concepts.

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Multi-Selectmedium

You are designing a monitoring strategy for a GKE cluster. You need to collect application metrics, traces, and logs in a vendor-neutral way, and export them to Google Cloud. Which TWO components should you use? (Choose two.)

Select 2 answers

A.Cloud Logging agent

B.Stackdriver Monitoring agent

C.OpenCensus agent

D.OpenTelemetry SDK

E.OpenTelemetry Collector

AnswersD, E

Vendor-neutral instrumentation for traces, metrics, and logs.

Why this answer

OpenTelemetry SDK is the vendor-neutral instrumentation library. The OTel Collector can receive data from SDKs and export to multiple backends including Cloud Monitoring, Cloud Trace, and Cloud Logging. Stackdriver agent is deprecated, and OpenCensus is legacy.

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MCQmedium

You need to set up a notification channel for alerts that sends messages to a custom internal incident management system via a webhook. Which notification channel type should you use?

A.PagerDuty

B.Cloud Pub/Sub

C.Email

D.SMS

AnswerB

Pub/Sub can be used to trigger a webhook via a subscriber.

Why this answer

Cloud Monitoring supports webhooks via Pub/Sub. You can create a Cloud Pub/Sub notification channel and subscribe with a webhook endpoint. Other channels like email, SMS, and PagerDuty are pre-defined but not customizable for webhooks.

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MCQhard

A team wants to create a log-based metric that counts ERROR log entries grouped by the service name extracted from a JSON field. The log entries are structured JSON with a 'service' key. Which type of log-based metric should they create?

A.Alerting policy with a condition on 'service' field

B.Gauge metric with the 'service' field as value

C.Counter metric with a label extracted from 'service' field

D.Distribution metric with a histogram of the 'service' field

AnswerC

Counter metrics count log entries matching a filter and can have labels extracted from log fields for grouping.

Why this answer

To count log entries and group by a label, a counter metric is appropriate. Distribution metrics are for histograms of numeric values, not counting.

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MCQmedium

You have a Cloud Monitoring alerting policy that triggers when CPU usage exceeds 80% for 5 minutes. You want to add documentation to the alert that includes a link to a runbook and labels indicating the affected service. How can you achieve this?

A.Create a separate Cloud Storage bucket and reference it in the alert.

B.Use Cloud Logging to store runbook URLs and attach them to alerts via log-based metrics.

C.Edit the alerting policy and add documentation with the runbook URL and labels under the 'Documentation' section.

D.Use Cloud Monitoring's notification channel to include a link in the email body.

AnswerC

Alert policy documentation supports markdown and labels can be added to the policy.

Why this answer

Cloud Monitoring alerting policies support documentation fields where you can provide markdown text including links and labels. This is done when creating or editing the policy in the console or via API. Labels can be attached to the alert policy to help with filtering and routing.

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MCQeasy

A DevOps engineer wants to create an alerting policy that fires when the 99th percentile of request latency exceeds 2000 ms for any 5-minute window. Which metric type and aggregation should they use?

A.Use a GAUGE metric with reducer MAX

B.Use a CUMULATIVE metric with reducer DELTA

C.Use a distribution metric with reducer 99th percentile

D.Use a DELTA metric with reducer COUNT

AnswerC

Distribution metrics support percentiles. Reducer set to 99th percentile yields the desired value.

Why this answer

The 99th percentile is a distribution metric. Cloud Monitoring supports distribution metrics and can compute percentiles. The correct approach is to use a metric of type distribution with reducer set to 99th percentile.

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MCQhard

A microservice running on Cloud Run is logging structured JSON payloads. You want to correlate logs with distributed traces using Cloud Trace. Which field should you include in the log entry to enable automatic correlation?

A.Set the 'labels' field with the trace ID

B.Set the 'httpRequest' field with a reference to the trace

C.Include the 'spanId' field with the span ID

D.Include the 'trace' field with the full trace URL

AnswerD

The trace field in the LogEntry is used to associate logs with traces.

Why this answer

The 'trace' field in the structured log entry should be set to the trace ID in the format 'projects/PROJECT_ID/traces/TRACE_ID'. This enables Cloud Logging to link logs to traces. The 'spanId' field is for span ID, but the trace field is required for correlation.

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MCQmedium

An engineer needs to troubleshoot a performance issue where a specific function in a Go application is consuming excessive CPU. They want to identify the exact lines of code causing the bottleneck with minimal overhead. Which Google Cloud tool should they use?

A.Error Reporting

B.Cloud Profiler

C.Cloud Monitoring

D.Cloud Trace

AnswerB

Cloud Profiler is designed for low-overhead CPU profiling and flame graph visualization.

Why this answer

Cloud Profiler continuously profiles CPU, heap, and other resources with very low overhead (~0.5%). It provides flame graphs that show which functions consume the most CPU, helping identify hot functions. Error Reporting focuses on errors, Cloud Trace on latency, and Cloud Monitoring on metrics.

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MCQhard

An application is emitting structured JSON logs that include a 'request_id' field used for correlation. The team wants to enable log correlation with Cloud Trace so they can navigate from a log entry to its trace. What must they include in the log entry?

A.The trace ID in a field named 'trace' in the JSON payload

B.A logging label with key 'trace' and value set to the trace ID

C.The span ID in a field named 'span'

D.The request ID in a field named 'request_id'

AnswerA

The 'trace' field in structured logs is used by Cloud Logging to link to Cloud Trace.

Why this answer

Cloud Logging automatically correlates logs with traces when the log entry contains the trace field with the correct format.

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MCQhard

You want to create a dashboard-as-code for Cloud Monitoring using the API. You have a JSON representation of the dashboard. Which command correctly creates the dashboard?

A.gcloud monitoring dashboards create --config-from-file=file.json

B.gcloud beta monitoring dashboards create --json-file=file.json

C.gcloud monitoring dashboards import file.json

D.gcloud monitoring dashboards create --dashboard="file.json"

AnswerA

This is the correct command.

Why this answer

The gcloud monitoring dashboards create command with the --config-from-file flag allows you to create a dashboard from a JSON file.

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MCQmedium

Your team has an alerting policy that fires frequently but the on-call engineer has difficulty understanding the impact. You want to add contextual information such as a runbook URL and severity label. What should you do?

A.Set a description on the notification channel

B.Create a separate alert policy for each severity level

C.Edit the alert policy documentation field to include a runbook URL and labels

D.Add labels to the metric descriptor used in the condition

AnswerC

Documentation field supports markdown and can include links and labels.

Why this answer

Alert policy documentation allows you to add plain text, including markdown, which can contain links to runbooks and other info. You can also add labels to the policy. Adding labels to the metric or using notification channel descriptions does not attach documentation to the alert.

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Multi-Selectmedium

You are building a dashboard in Cloud Monitoring to display CPU utilization of Compute Engine instances. Which TWO chart types are appropriate for showing trends over time? (Select 2)

Select 2 answers

A.Stacked bar

B.Line

C.Scatter

D.Scorecard

E.Heatmap

AnswersA, B

Stacked bar charts can show contributions over time.

Why this answer

Line charts and stacked bar charts are commonly used to show trends over time. Heatmaps show distributions, scatter plots show correlations, and scorecards show a single value.

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MCQmedium

A company wants to set up an alert that fires if any Compute Engine instance has a CPU utilization above 80% for more than 5 minutes. They have thousands of instances. Which alerting condition type and configuration is most efficient?

A.Use a logs-based alert on CPU logs

B.Use a metric-threshold condition with resource group filter set to 'gce_instance' and a threshold of 80%

C.Create a separate alert for each instance using instance-specific conditions

D.Use a metric-absent condition to detect when CPU metric stops reporting

AnswerB

A single alert can monitor all instances by using a resource group filter.

Why this answer

A metric-threshold condition using a policy with multiple conditions (one per instance) would be cumbersome. Instead, use a grouped alert with a threshold on the metric and apply to all instances via a monitored resource filter.

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MCQmedium

A DevOps engineer wants to monitor the CPU utilization of a Compute Engine instance and receive an alert if it exceeds 80% for more than 5 minutes. Which type of metric should be used in the alerting condition?

A.CUMULATIVE

B.DELTA

C.COUNT

D.GAUGE

AnswerD

CPU utilization is a gauge metric that represents a value at a point in time.

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MCQeasy

Which Google Cloud service provides continuous, low-overhead profiling of CPU and memory usage to identify performance bottlenecks in production?

A.Error Reporting

B.Cloud Monitoring

C.Cloud Trace

D.Cloud Profiler

AnswerD

Cloud Profiler provides continuous profiling with low overhead (0.5%).

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MCQmedium

You need to set up an alert that fires when any of your Compute Engine instances has been down (no metric data) for more than 10 minutes. Which condition type should you use?

A.Metric absent

B.Metric threshold

C.Log match

D.Forecast

AnswerA

Correct. Metric absent fires when data stops reporting for a given duration.

Why this answer

The 'metric absent' condition fires when a metric stops reporting data for a specified duration. This is ideal for detecting instance downtime. 'Metric threshold' requires a value above/below a threshold; 'forecast' predicts future values; 'log match' is for log-based alerts (not applicable for instance up/down).

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MCQmedium

A company wants to visualize request latency distribution across all services using a heatmap. Which Cloud Monitoring chart type should be used?

A.Scorecard

B.Heatmap

C.Line chart

D.Stacked bar chart

AnswerB

Heatmaps show distributions over time with color intensity.

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MCQeasy

Which notification channel can be used with Cloud Monitoring to trigger a custom workflow in Cloud Functions when an alert fires?

A.Slack via Pub/Sub

B.PagerDuty

C.Email

D.Cloud Pub/Sub

AnswerD

Pub/Sub can trigger Cloud Functions, Cloud Run, etc. for custom workflows.

Why this answer

Cloud Pub/Sub is a notification channel that can trigger downstream services like Cloud Functions.

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MCQeasy

An application running on Google Kubernetes Engine (GKE) uses OpenTelemetry SDK to export traces. Which Google Cloud service should receive these traces?

A.Cloud Logging

B.Cloud Monitoring

C.Error Reporting

D.Cloud Trace

AnswerD

Correct. Cloud Trace is the distributed tracing service for Google Cloud.

Why this answer

Cloud Trace is the managed distributed tracing service on Google Cloud. OpenTelemetry SDK can export traces directly to Cloud Trace via the exporter. Cloud Monitoring is for metrics, Cloud Logging for logs, and Error Reporting for exceptions.

Traces go to Cloud Trace.

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Multi-Selectmedium

You want to use Error Reporting to monitor exceptions in your application. Which TWO prerequisites must be met? (Select 2)

Select 2 answers

A.Enable Error Reporting API for the project.

B.Use a supported language runtime.

C.Log exceptions to Cloud Logging with stack traces.

D.Create a Pub/Sub topic for errors.

E.Install the Cloud Profiler agent.

AnswersA, C

The API must be enabled.

Why this answer

Error Reporting requires that exceptions are logged to Cloud Logging with stack traces, and the service must be configured to write log entries with the appropriate format.

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Multi-Selecthard

You need to set up a logs-based alert that triggers when the 99th percentile of request latency exceeds 500ms in a 10-minute window. You create a logs-based distribution metric. Which THREE alert conditions are needed? (Select 3)

Select 3 answers

A.Reducer: 99th percentile

B.Alignment period: 10 minutes

C.Notification channel: SMS only

D.Metric type: Log-based distribution metric

E.Condition type: Metric absence

AnswersA, B, D

To compute the 99th percentile.

Why this answer

To alert on a percentile of a distribution metric, you need to use the distribution metric, configure the percentile reducer (99th), set an alignment period, and define a threshold condition.

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MCQhard

An application running on GKE uses OpenTelemetry for instrumentation. You need to export traces to Cloud Trace and custom metrics to Cloud Monitoring. Which approach is most efficient?

A.Write a custom exporter in the application that sends data to both services

B.Export traces to Cloud Trace via Stackdriver Trace exporter and metrics to Cloud Monitoring via Stackdriver Metrics exporter in the SDK

C.Configure the OpenTelemetry SDK to export directly to Cloud Trace and Cloud Monitoring endpoints

D.Use the OpenTelemetry Collector to receive OTLP data and export to Cloud Trace and Cloud Monitoring

AnswerD

The Collector is the recommended approach for multi-backend export, providing robustness and flexibility.

Why this answer

The OpenTelemetry Collector can receive OTLP data from the application and export to multiple backends. Using the Collector as a sidecar or daemonset reduces overhead and provides buffering. Direct export from the SDK increases complexity and resource usage.

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MCQhard

You are configuring a log-based metric that counts error log entries with severity ERROR. You need to set up an alert that fires when the count exceeds 100 in a 5-minute window. Which alert condition type and reducer should you use?

A.Metric absence condition with reducer COUNT and alignment period 5 minutes

B.Metric threshold condition with reducer MEAN and alignment period 5 minutes

C.Forecast condition with reducer SUM and alignment period 5 minutes

D.Metric threshold condition with reducer COUNT and alignment period 5 minutes

AnswerB

MEAN reducer on a cumulative metric gives the rate, which can be compared to a threshold.

Why this answer

For log-based counter metrics, you use a metric threshold condition. The metric type is a counter (cumulative), so you need to compute the rate of change using the MEAN reducer (which for a counter gives the rate) to compare against a threshold.

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MCQeasy

You need to create a custom metric in Cloud Monitoring that measures the number of HTTP requests to your application each second. Which metric type should you use?

A.DELTA

B.GAUGE

C.CUMULATIVE

D.COUNT

AnswerA

Delta reports the change over time, suitable for counting events per second.

Why this answer

A DELTA metric reports the change in a value over time, which is appropriate for counting events per second. GAUGE reports a value at a point in time, and CUMULATIVE reports an accumulating total.

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Multi-Selecteasy

Which TWO metrics are examples of GAUGE metric types? (Select 2)

Select 2 answers

A.CPU utilization

B.Total number of active users

C.Disk read bytes per second

D.Memory usage

E.Request count per second

AnswersA, D

CPU utilization is a gauge.

Why this answer

GAUGE metrics represent a value at a point in time, such as CPU usage or memory usage. Request count is a DELTA or CUMULATIVE metric, and disk read bytes per second is a DELTA metric.

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MCQmedium

A team needs to create an alert that triggers when the 99th percentile latency of a service exceeds 500ms for at least 10 minutes. Which alerting configuration is correct?

A.Absent condition with duration 10 min, based on 'latency' metric.

B.Metric threshold condition with alignment period 10 min, reducer MEAN, based on 'latency' metric.

C.Metric threshold condition with alignment period 10 min, reducer PERCENTILE_99, based on 'latency' metric.

D.Metric threshold condition with alignment period 10 min, reducer COUNT, based on 'latency' metric.

AnswerC

PERCENTILE_99 reducer computes the 99th percentile over the alignment period.

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MCQmedium

A company uses Cloud Profiler to analyze CPU usage of a Java application running on GKE. They see a function consuming 40% of CPU time. To investigate further, they want to see the exact line numbers in the source code. What should they do?

A.Use Cloud Trace to trace the function call

B.Enable debug symbols in the application and upload source code to Cloud Profiler

C.Enable structured logging for the application

D.Switch to a different profiling tool

AnswerB

Source code upload allows Profiler to map addresses to line numbers.

Why this answer

Cloud Profiler requires the source code to be available and configured for symbolication. The flame graph shows function names, but line numbers require source context.

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MCQmedium

An application running on GKE needs to send custom metrics to Cloud Monitoring. The team wants to use a vendor-neutral instrumentation approach that also supports traces and logs. Which solution should they choose?

A.Use the Cloud Monitoring API directly from the application to write metrics

B.Use OpenTelemetry SDK and OTel Collector to export to Cloud Monitoring and Cloud Trace

C.Use Prometheus client library and scrape metrics with Managed Service for Prometheus

D.Use the Stackdriver Monitoring client library

AnswerB

OpenTelemetry is vendor-neutral and the Collector can export to multiple backends including Google Cloud.

Why this answer

OpenTelemetry is a vendor-neutral standard for observability (metrics, traces, logs). The OpenTelemetry Collector can run as a DaemonSet on GKE and export data to Cloud Monitoring and Cloud Trace.

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MCQhard

A company uses Cloud Profiler to identify performance bottlenecks. They notice that the CPU profile shows a function consuming high CPU, but they want to see the corresponding source code lines. What must they enable?

A.Enable flame graphs in the Profiler settings

B.Enable debug symbols in the application build

C.Instrument the application with OpenTelemetry SDK

D.Increase the Profiler sampling rate

AnswerB

Correct. Debug symbols (e.g., -g for Go, or /DEBUG for .NET) allow Profiler to map addresses to source lines.

Why this answer

Cloud Profiler can display source code locations if the application is built with debug symbols and the source code is available. For Google Cloud services, it automatically shows line numbers. For custom applications, you need to ensure the binary includes debug information (e.g., -g flag for Go, or pdb files for .NET).

Profiler does not require OpenTelemetry, Flame graphs are a visualization, not a requirement, and sampling rate is unrelated.

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MCQmedium

You are designing a dashboard for application latency. You want to visualize the distribution of latency values across different services using a chart that shows percentiles over time. Which chart type should you use?

A.Scatter chart

B.Stacked bar chart

C.Line chart

D.Heatmap chart

AnswerD

Heatmaps display distributions over time, ideal for visualizing percentiles and patterns.

Why this answer

A heatmap chart can show the distribution of values over time, with color intensity representing count. It allows visualizing percentiles and patterns. Line charts show trends, stacked bar shows component totals, and scatter shows individual data points.

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Multi-Selecthard

You are designing a monitoring strategy for a hybrid cloud environment where on-premises services send metrics to Cloud Monitoring via the Monitoring API. The on-premises network has limited outbound connectivity and can only reach Google Cloud through a few static IPs. You need to ensure that custom metrics are ingested reliably and cost-effectively. Which THREE actions should you take? (Choose 3)

Select 3 answers

A.Use a service account with roles/monitoring.metricWriter on the on-premises application

B.Enable Cloud NAT to provide outbound connectivity

C.Configure VPC Service Controls to restrict data exfiltration

D.Set up a Cloud VPN tunnel between on-premises and VPC

E.Use Cloud Monitoring's Agent to pull metrics from on-premises

AnswersA, C, D

The service account is used to authenticate API calls from on-premises.

Why this answer

To handle limited connectivity, using a Cloud VPN tunnel ensures secure and reliable connectivity. The Monitoring API requires authentication via a service account, and a VPC Service Controls perimeter can protect data exfiltration. Pushing metrics from on-premises is the standard approach.

Pulling is not supported.

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MCQmedium

Your team uses PagerDuty for incident notifications. You need to configure Cloud Monitoring to send alerts to PagerDuty. Which notification channel type should you select?

A.Slack

B.Cloud Pub/Sub

C.SMS

D.PagerDuty

AnswerD

PagerDuty is a supported notification channel type in Cloud Monitoring.

Why this answer

Cloud Monitoring supports PagerDuty as a notification channel type directly. You can configure it by providing the PagerDuty integration key. No other channel types (email, Slack, Pub/Sub) are needed.

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Multi-Selecthard

You need to reduce the cost of Cloud Monitoring custom metrics while retaining the ability to create alerts on those metrics. Which THREE actions should you take? (Choose three.)

Select 3 answers

A.Use GAUGE metric kind instead of DELTA or CUMULATIVE

B.Use OpenTelemetry SDK to batch metric reports

C.Set a longer alignment period in your alerting policy

D.Report custom metrics every 60 seconds instead of every 10 seconds

E.Use distribution metric kind to aggregate data

AnswersA, B, D

GAUGE metrics only report when value changes, reducing samples.

Why this answer

Custom metrics are charged per sample. Using GAUGE metrics reduces sample count as DELTA and CUMULATIVE require periodic reporting. Increasing the sampling interval reduces volume.

Using OpenTelemetry allows batching. Setting longer alignment periods in alerts reduces query cost. Using distribution metrics may increase cost due to multiple buckets.

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MCQmedium

You need to monitor the number of ERROR-level log entries in Cloud Logging and trigger an alert when the count exceeds 100 in 5 minutes. Which approach should you use?

A.Create a log-based distribution metric and set an alert on the count

B.Use a logs-based alerting policy directly from Cloud Logging

C.Create a log-based counter metric with filter 'severity=ERROR' and then set an alert on that metric

D.Export logs to BigQuery and run a scheduled query to trigger a custom alert

AnswerC

Log-based metrics can count log entries, and alerts can be set on them.

Why this answer

Log-based metrics allow you to define a counter metric that counts log entries matching a filter. You can then create an alerting policy on that metric. Logs-based alerting directly from logs is not available; you must use a log-based metric.

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Multi-Selectmedium

A DevOps team needs to set up alerting for a critical application that runs on Compute Engine. They want to be notified if the application process crashes (i.e., stops sending heartbeats) for more than 5 minutes. The application emits a custom metric 'app_heartbeat' as a GAUGE with value 1 every 60 seconds. Which TWO configurations should they use? (Choose 2)

Select 2 answers

A.Alignment period: 60 seconds

B.Duration: 5 minutes

C.Metric threshold condition: threshold < 1

D.Reducer: MEAN

E.Metric absent condition: for 'app_heartbeat' metric

AnswersB, E

The duration for which the metric must be absent (or condition met) before alerting.

Why this answer

To detect a crash, you need a metric absent condition (no data for 5 minutes) because the gauge value will stop reporting. Setting the duration to 5 minutes ensures timely notification. A threshold condition on the gauge value would not trigger if the value stays at 1.

The alignment period and reducer are not relevant for absent conditions.

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MCQmedium

You want to visualize the 99th percentile latency of a service on a dashboard. The metric is a distribution metric. Which reducer should you use in the chart configuration?

A.MEAN

B.SUM

C.99TH_PERCENTILE

D.COUNT

AnswerC

This reducer computes the 99th percentile.

Why this answer

The 99th percentile is a specific percentile, not a simple aggregation like MEAN, SUM, or COUNT. Cloud Monitoring charts can use percentile reducers. Among the options, '99TH_PERCENTILE' is the correct one.

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MCQeasy

A DevOps engineer wants to create a dashboard that shows the number of 5xx errors per service over time. The errors are logged in Cloud Logging. What is the most efficient way to create this dashboard?

A.Create a log-based counter metric for 5xx errors, then add a chart in Cloud Monitoring using that metric.

B.Write a custom application that sends the error count to Cloud Monitoring as a custom metric via API.

C.Export logs to BigQuery and use Data Studio to create a dashboard.

D.Use Cloud Logging's metrics explorer to create a chart and embed it in a dashboard.

AnswerA

Log-based metrics are efficient and can be used in dashboards directly.

Why this answer

First, create a log-based metric that counts log entries with severity ERROR and status code 5xx. Then, in Cloud Monitoring, create a dashboard with a chart using that metric, grouped by service. This avoids querying logs directly and provides a reusable metric.

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MCQmedium

Your team uses Cloud Monitoring to alert on high CPU usage for Compute Engine instances. You want to be notified via email and Slack. You have created a notification channel for email. What must you do to also notify Slack?

A.Create a second email notification channel with the Slack email integration address.

B.Create a Cloud Pub/Sub notification channel and set up a subscription to post messages to Slack via webhook.

C.Use Cloud Logging to forward logs to Slack directly.

D.Install the Stackdriver Slack app and configure it to receive alerts via Cloud Monitoring API.

AnswerB

This is the correct approach as described.

Why this answer

While Cloud Monitoring supports a direct Slack notification channel via webhook, it is also possible to use Cloud Pub/Sub to send notifications to Slack. This involves creating a Pub/Sub notification channel and configuring a subscription to post messages to Slack via webhook. Option B correctly describes this alternative method.

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MCQhard

You are configuring a dashboard-as-code using the Cloud Monitoring API. You want to create a dashboard that shows a heatmap of request latency distribution across all services. Which chart type and aggregation should you use?

A.Heatmap chart using a distribution metric

B.Stacked bar chart with mean aggregation

C.Line chart with 95th percentile aggregation

D.Scorecard chart with MAX aggregation

AnswerA

Correct. Heatmaps visualize the distribution of values across buckets over time.

Why this answer

Heatmaps in Cloud Monitoring are created using a 'heatmap' chart type, which displays the distribution of metric values over time. This requires a distribution metric (e.g., from OpenTelemetry or custom) that records a histogram. The heatmap visualizes percentiles or count per bucket over time.

Line, stacked bar, and scorecard cannot show distribution.

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MCQeasy

You want to automatically group and track similar errors in your application logs, and see trends over time. Which Google Cloud service should you use?

A.Error Reporting

B.Cloud Monitoring

C.Cloud Logging

D.Cloud Trace

AnswerA

Error Reporting automatically groups similar exceptions and tracks trends.

Why this answer

Error Reporting automatically groups exceptions by similarity, provides trend analysis, and links to logs and traces. Cloud Monitoring is for metrics, Cloud Logging for logs, and Cloud Trace for tracing.

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MCQmedium

A DevOps engineer wants to visualize the 99th percentile latency of an HTTP endpoint over the past week using Cloud Monitoring dashboards. The metric is available as a distribution from Cloud Trace. Which chart type should they use to display this percentile over time?

A.Heatmap

B.Line chart

C.Stacked bar chart

D.Scatter chart

AnswerB

Line charts are optimal for displaying continuous data points over time, such as percentile values.

Why this answer

A line chart is best for showing a metric value (like 99th percentile latency) over time. Heatmaps are for distributions, scatter plots show correlation, and stacked bars show contributions of components. The percentile over time is a single value per time point, so a line chart is appropriate.

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MCQeasy

You need to create a custom metric to monitor the number of user logins per minute. Which metric kind should you use?

A.DELTA

B.GAUGE

C.Counter

D.CUMULATIVE

AnswerD

CUMULATIVE measures a monotonically increasing count over time, suitable for login counts.

Why this answer

A CUMULATIVE metric measures a count over time that increases monotonically, such as total logins. GAUGE measures an instantaneous value, DELTA measures a value over an interval, and a counter is not a metric kind.

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MCQmedium

Which notification channel is supported by Cloud Monitoring for alerting without additional third-party integrations?

A.PagerDuty

B.Slack

C.SMS

D.Email

AnswerD

Email is a built-in notification channel in Cloud Monitoring.

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MCQhard

Your team is using Cloud Monitoring dashboards to monitor a multi-service architecture. You want to manage dashboards as code using Terraform. Which approach should you use to create a dashboard?

A.Use the Monitoring API directly from Terraform with a custom provider

B.Use the google_monitoring_dashboard Terraform resource with a dashboard JSON configuration

C.Create the dashboard manually and export it as a JSON file, then import into Terraform

D.Use gcloud alpha monitoring dashboards create command with a YAML file

AnswerB

This is the correct way to manage dashboards as code with Terraform.

Why this answer

Cloud Monitoring dashboards can be managed via the Dashboard API using a JSON configuration. Terraform has a google_monitoring_dashboard resource that accepts a JSON or YAML representation of the dashboard. The gcloud command only supports exporting/importing, not declarative management.

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MCQmedium

You need to set up a notification channel for alerting that triggers a PagerDuty incident. The PagerDuty integration key is 'abc123'. What is the correct command to create this channel?

A.gcloud alpha monitoring channels create --type=slack --display-name="PagerDuty" --channel-labels=token=abc123

B.gcloud alpha monitoring channels create --type=pagerduty --display-name="PagerDuty" --channel-labels=auth_token=abc123

C.gcloud alpha monitoring channels create --type=pagerduty --display-name="PagerDuty" --channel-labels=service_key=abc123

D.gcloud beta monitoring channels create --type=webhook --display-name="PagerDuty" --channel-labels=url=https://events.pagerduty.com/integration/abc123/enqueue

AnswerC

Correct. This creates a PagerDuty notification channel with the integration key.

Why this answer

PagerDuty notification channels require the type 'pagerduty' and a 'service_key' (integration key) in the labels. The gcloud command is 'gcloud alpha monitoring channels create' with --type=pagerduty and --display-name and --channel-labels=service_key=... .

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MCQeasy

You want to continuously profile the CPU usage of a production application running on Compute Engine to identify performance bottlenecks. Which Google Cloud service should you use?

A.Cloud Trace

B.Cloud Monitoring

C.Cloud Profiler

D.Error Reporting

AnswerC

Cloud Profiler is designed for continuous profiling of CPU, heap, threads, and contention.

Why this answer

Cloud Profiler provides continuous, low-overhead profiling for CPU, heap, threads, and contention. It uses statistical sampling and presents results in flame graphs. Cloud Trace is for distributed tracing, not profiling.

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MCQeasy

A team wants to visualize real-time CPU utilization across a fleet of Compute Engine instances in a single dashboard. Which chart type in Cloud Monitoring is most appropriate?

A.Stacked bar chart

B.Scatter plot

C.Heatmap

D.Line chart

AnswerD

Correct. Line charts are designed for time-series data, showing how metrics change over time.

Why this answer

Line charts are ideal for time-series data like CPU utilization over time, showing trends and variations. Stacked bar charts are better for categorical comparisons, heatmaps for density, and scatter plots for correlation. For multiple instances over time, line charts are standard.

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Multi-Selectmedium

A team wants to use Cloud Profiler to identify CPU hot functions in a production service. Which TWO statements about Cloud Profiler are correct? (Choose 2)

Select 2 answers

A.It provides flame graphs to visualize function call stacks

B.It can profile only Java applications

C.It has a typical overhead of about 0.5% of CPU

D.It requires modifying application code to add profiling calls

E.It profiles all functions by default with no configuration

AnswersA, C

Flame graphs are a key visualization in Cloud Profiler.

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MCQmedium

Your application uses structured logging in JSON format. You want to ensure that each log entry is automatically correlated with the corresponding trace in Cloud Trace. Which field must be included in the JSON payload?

A."severity" field set to "ERROR"

B."httpRequest" object with requestUrl and status

C."trace" field with the trace ID formatted as projects/PROJECT_ID/traces/TRACE_ID

D."labels" field containing custom metadata

AnswerC

Correct. The trace field in structured logs enables correlation with Cloud Trace.

Why this answer

Cloud Logging correlates logs with traces via the 'logging.googleapis.com/trace' field (or 'trace' in the LogEntry), which should contain the trace ID in the format 'projects/[PROJECT_ID]/traces/[TRACE_ID]'. The 'httpRequest' field is for HTTP request data, 'severity' for log level, and 'labels' for custom metadata. Only the trace field provides correlation.

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MCQhard

You need to monitor a critical batch job that runs daily on Compute Engine. If the job does not complete within 2 hours, you want to be paged via PagerDuty. The job emits a custom metric 'batch_duration' as a GAUGE that records the duration of the last completed run. What alerting policy condition should you use?

A.Forecast condition: predict that duration will exceed 2 hours.

B.Metric absent condition: with duration of 2 hours on the 'batch_duration' metric.

C.Log-based metric alert: count log entries indicating job failure.

D.Metric threshold condition: threshold > 7200 (seconds) on the 'batch_duration' metric.

AnswerB

This triggers if the metric stops reporting for 2 hours, indicating the job did not complete.

Why this answer

Since the metric only has a value when the job completes, you need to detect the absence of data. If the job fails or doesn't run, the metric will stop reporting. A 'metric absent' condition triggers if no data is received for a specified duration (e.g., 2 hours).

This is more reliable than a threshold on the gauge value, which might still show an old value.

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MCQmedium

A DevOps engineer needs to create a dashboard as code for Cloud Monitoring. They want to version-control the dashboard definition. Which method should they use?

A.Use gcloud alpha monitoring dashboards create command with a JSON file

B.Create the dashboard via the Cloud Console and export the JSON representation

C.Use Grafana with Cloud Monitoring data source and export dashboard JSON

D.Use the Cloud Monitoring API to create dashboards from a Terraform resource

AnswerD

Terraform supports google_monitoring_dashboard resource, allowing infrastructure-as-code for dashboards.

Why this answer

Cloud Monitoring dashboards can be defined using the REST API or gcloud commands, and the JSON/YAML definition can be stored in version control.

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MCQhard

An engineer wants to create a dashboard that shows the number of HTTP 5xx errors per minute from an application that writes structured logs. The application logs are in Cloud Logging. Which approach should be used?

A.Export logs to BigQuery and run a query every minute to count errors.

B.Use the Cloud Monitoring API to create a custom metric for error counts from logs.

C.Create an alert policy that triggers on error logs and counts them via a notification channel.

D.Create a log-based metric with a counter for log entries containing '5xx' and use it in a dashboard.

AnswerD

Log-based counter metrics count matching log entries per minute.

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Multi-Selecteasy

A company wants to use Cloud Logging to monitor for a specific error pattern in their application logs. They want to create a metric that counts occurrences of the error and then set up an alert when the count exceeds 100 in 5 minutes. Which TWO components are required? (Choose 2)

Select 2 answers

A.Notification channel (e.g., email)

B.Log-based distribution metric

C.Alerting policy with metric threshold condition

D.Cloud Monitoring dashboard

E.Log-based counter metric

AnswersC, E

Triggers when the counter metric exceeds 100 over a sliding window.

Why this answer

A log-based counter metric counts matching log entries. An alerting policy can then be configured to trigger when the metric exceeds a threshold. The metric itself does not need to be distribution-based (that's for histogram values).

The alignment period is part of the alert condition, not a separate component.

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Multi-Selecthard

An engineer is configuring an alerting policy for a latency metric. They want to reduce noise by requiring that the condition be met for at least 3 out of the last 5 alignment periods. Which settings must be adjusted? (Choose 3)

Select 3 answers

A.Alignment period

B.Duration

C.Number of violation periods

D.Evaluation frequency

E.Condition threshold

AnswersA, C, D

The alignment period defines the window for each data point.

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MCQmedium

You are configuring an alerting policy in Cloud Monitoring to notify when the 99th percentile latency of your application exceeds 500 ms for 5 minutes. Which metric type and reducer should you use?

A.DISTRIBUTION metric type with PERCENTILE reducer

B.CUMULATIVE metric type with MEAN reducer

C.GAUGE metric type with MAX reducer

D.DELTA metric type with SUM reducer

AnswerA

Distribution metrics record histograms, and the 99th percentile reducer calculates the percentile value from the distribution.

Why this answer

The 99th percentile requires a distribution metric that records a histogram of latency values. The reducer 'PERCENTILE' (not listed as a standard option, but the correct approach is to use a distribution metric with '99th PERCENTILE' reducer) is needed. However, Cloud Monitoring alerting does not support percentile reducers directly; you must use a log-based metric or export to a different system.

In this exam context, the correct answer is to use a distribution metric with a percentile aggregation. Among the options, only 'distribution' with a percentile reducer is correct.

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MCQhard

An application running on Cloud Run is experiencing high tail latency. The team wants to visualize which functions are consuming the most CPU time. Which Google Cloud tool should they use and how should they instrument the application?

A.Cloud Profiler with OpenTelemetry or Profiler agent

B.Cloud Monitoring with custom metrics for CPU

C.Cloud Trace with automatic instrumentation

D.Cloud Logging with structured logs

AnswerA

Cloud Profiler provides flame graphs showing CPU consumption per function, ideal for identifying hot functions.

Why this answer

Cloud Profiler provides continuous profiling (CPU, heap, etc.) with low overhead. For Cloud Run, the Profiler can be enabled via the Cloud Profiler Java agent or OpenTelemetry.

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MCQeasy

Error Reporting automatically groups similar exceptions. Which Google Cloud services does Error Reporting integrate with to provide additional context?

A.Cloud Trace and Cloud Profiler

B.Cloud Logging and Cloud Monitoring

C.Cloud Logging and Cloud Trace

D.Cloud Monitoring and Cloud Profiler

AnswerC

Correct. Error Reporting links to logs and traces for each error group.

Why this answer

Error Reporting integrates with Cloud Logging to show log entries associated with errors, and with Cloud Trace to show trace details. Cloud Monitoring provides metrics, but not direct context per error. Cloud Profiler is for CPU/memory profiles, not error context.

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MCQeasy

Which Cloud Monitoring dashboard chart type is best suited to show the distribution of request latencies across multiple services over time?

A.Scorecard

B.Line chart

C.Heatmap

D.Stacked bar chart

AnswerC

Heatmaps show distribution across two axes with color intensity, perfect for latency distributions.

Why this answer

Heatmap charts are ideal for showing distribution across two dimensions (e.g., service and time) with color intensity representing count or latency.

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Multi-Selectmedium

You are investigating a performance issue in a distributed application. You want to identify the services causing high latency. Which TWO tools should you use together? (Choose two.)

Select 2 answers

A.Error Reporting

B.Cloud Trace

C.Cloud Profiler

D.Cloud Logging

E.Cloud Monitoring

AnswersB, C

Provides trace analysis and latency distribution.

Why this answer

Cloud Trace analyzes latency distributions and identifies slow requests. Cloud Profiler identifies hot functions consuming CPU/memory. Cloud Monitoring shows metrics but not traces, Cloud Logging shows logs, and Error Reporting shows errors.

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MCQhard

You need to set up an alert that fires when the total number of errors in a specific Cloud Logging log view exceeds 10 in any 5-minute window over the last 1 hour. Which combination of alerting policy conditions and log-based metric is appropriate?

A.Use a forecast condition on the log-based metric to predict when errors will exceed 10

B.Create a log-based counter metric and use a metric threshold condition with rolling window 5 minutes, threshold 10

C.Use a logs-based alerting policy directly with a filter and set count > 10 in 5 minutes

D.Create a metric threshold alert on the log entry count metric with duration 5 minutes and threshold 10

AnswerB

Correct. Log-based counter metrics count log entries; alert threshold condition evaluates on rolling window.

Why this answer

To count errors from logs, you create a log-based counter metric. Then set an alert with condition type 'metric threshold', using the counter metric, with aggregation window '5 minutes', and trigger if value > 10. The 'condition absent' is for missing data, not threshold. 'Forecast' predicts future values.

Standard configuration: log-based counter metric + metric threshold alert.

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