Question # 1
| Clicking a metric name from the results in metric finder displays the metric in Chart Builder.
What action needs to be taken in order to save the chart created in the UI? |
| A. Create a new dashboard and save the chart.
| | B. Save the chart to multiple dashboards.
| | C. Make sure that data is coming in for the metric then save the chart.
| | D. Save the chart to a dashboard. |
D. Save the chart to a dashboard.
Explanation:
According to the web search results, clicking a metric name from the results in metric finder
displays the metric in Chart Builder1. Chart Builder is a tool that allows you to create and
customize charts using metrics, dimensions, and analytics functions2. To save the chart
created in the UI, you need to do the following steps:
Click the Save button on the top right corner of the Chart Builder. This will open a
dialog box where you can enter the chart name and description, and choose the
dashboard where you want to save the chart.
Enter a name and a description for your chart. The name should be descriptive
and unique, and the description should explain the purpose and meaning of the
chart.
Choose an existing dashboard from the drop-down menu, or create a new
dashboard by clicking the + icon. A dashboard is a collection of charts that display
metrics and events for your services or hosts3. You can organize and share
dashboards with other users in your organization using dashboard groups3.
Click Save. This will save your chart to the selected dashboard and redirect you to
the dashboard view. You can also access your saved chart from the Dashboards
menu on the left navigation bar.
Question # 2
| Which of the following can be configured when subscribing to a built-in detector? |
| A. Alerts on team landing page.
| | B. Alerts on a dashboard.
| | C. Outbound notifications.
| | D. Links to a chart. |
C. Outbound notifications.
According to the web search results1, subscribing to a built-in detector is a way to receive
alerts and notifications from Splunk Observability Cloud when certain criteria are met. A
built-in detector is a detector that is automatically created and configured by Splunk
Observability Cloud based on the data from your integrations, such as AWS, Kubernetes,
or OpenTelemetry1. To subscribe to a built-in detector, you need to do the following steps: -
Find the built-in detector that you want to subscribe to. You can use the metric
finder or the dashboard groups to locate the built-in detectors that are relevant to
your data sources1.
-
Hover over the built-in detector and click the Subscribe button. This will open a
dialog box where you can configure your subscription settings1.
-
Choose an outbound notification channel from the drop-down menu. This is where
you can specify how you want to receive the alert notifications from the built-in
detector. You can choose from various channels, such as email, Slack, PagerDuty,
webhook, and so on2. You can also create a new notification channel by clicking
the + icon2.
-
Enter the notification details for the selected channel. This may include your email
address, Slack channel name, PagerDuty service key, webhook URL, and so
on2. You can also customize the notification message with variables and
markdown formatting2.
-
Click Save. This will subscribe you to the built-in detector and send you alert
notifications through the chosen channel when the detector triggers or clears an
alert.
Therefore, option C is correct.
Question # 3
| Which component of the OpenTelemetry Collector allows for the modification of metadata? |
| A. Processors | | B. Pipelines | | C. Exporters | | D. Receivers |
A. Processors
Explanation: The component of the OpenTelemetry Collector that allows for the
modification of metadata is A. Processors.
Processors are components that can modify the telemetry data before sending it to
exporters or other components. Processors can perform various transformations on
metrics, traces, and logs, such as filtering, adding, deleting, or updating attributes, labels,
or resources. Processors can also enrich the telemetry data with additional metadata from
various sources, such as Kubernetes, environment variables, or system information1.
For example, one of the processors that can modify metadata is the attributes processor.
This processor can update, insert, delete, or replace existing attributes on metrics or
traces. Attributes are key-value pairs that provide additional information about the telemetry
data, such as the service name, the host name, or the span kind2.
Another example is the resource processor. This processor can modify resource attributes
on metrics or traces. Resource attributes are key-value pairs that describe the entity that
produced the telemetry data, such as the cloud provider, the region, or the instance type3.
Question # 4
| What is one reason a user of Splunk Observability Cloud would want to subscribe to an
alert? |
| A. To determine the root cause of the Issue triggering the detector.
| | B. To perform transformations on the data used by the detector.
| | C. To receive an email notification when a detector is triggered.
| | D. To be able to modify the alert parameters. |
C. To receive an email notification when a detector is triggered.
Explanation: One reason a user of Splunk Observability Cloud would want to subscribe to
an alert is C. To receive an email notification when a detector is triggered.
A detector is a component of Splunk Observability Cloud that monitors metrics or events
and triggers alerts when certain conditions are met. A user can create and configure
detectors to suit their monitoring needs and goals1.
A subscription is a way for a user to receive notifications when a detector triggers an alert.
A user can subscribe to a detector by entering their email address in the Subscription tab of
the detector page. A user can also unsubscribe from a detector at any time2.
When a user subscribes to an alert, they will receive an email notification that contains
information about the alert, such as the detector name, the alert status, the alert severity,
the alert time, and the alert message. The email notification also includes links to view the
detector, acknowledge the alert, or unsubscribe from the detector2.
Question # 5
| When installing OpenTelemetry Collector, which error message is indicative that there is a
misconfigured realm or access token? |
| A. 403 (NOT ALLOWED)
| | B. 404 (NOT FOUND)
| | C. 401 (UNAUTHORIZED)
| | D. 503 (SERVICE UNREACHABLE) |
C. 401 (UNAUTHORIZED)
Explanation: The correct answer is C. 401 (UNAUTHORIZED).
According to the web search results, a 401 (UNAUTHORIZED) error message is indicative
that there is a misconfigured realm or access token when installing OpenTelemetry
Collector1. A 401 (UNAUTHORIZED) error message means that the request was not
authorized by the server due to invalid credentials. A realm is a parameter that specifies
the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An
access token is a credential that grants access to a resource, such as a Splunk
Observability Cloud API. If the realm or the access token is misconfigured, the request to
install OpenTelemetry Collector will be rejected by the server with a 401
(UNAUTHORIZED) error message.
Option A is incorrect because a 403 (NOT ALLOWED) error message is not indicative that
there is a misconfigured realm or access token when installing OpenTelemetry Collector. A
403 (NOT ALLOWED) error message means that the request was authorized by the server
but not allowed due to insufficient permissions. Option B is incorrect because a 404 (NOT
FOUND) error message is not indicative that there is a misconfigured realm or access
token when installing OpenTelemetry Collector. A 404 (NOT FOUND) error message
means that the request was not found by the server due to an invalid URL or resource.
Option D is incorrect because a 503 (SERVICE UNREACHABLE) error message is not
indicative that there is a misconfigured realm or access token when installing
OpenTelemetry Collector. A 503 (SERVICE UNREACHABLE) error message means that
the server was unable to handle the request due to temporary overload or maintenance.
Question # 6
| Which of the following is optional, but highly recommended to include in a datapoint? |
| A. Metric name
| | B. Timestamp
| | C. Value
| | D. Metric type |
D. Metric type
Explanation: The correct answer is D. Metric type.
A metric type is an optional, but highly recommended field that specifies the kind of
measurement that a datapoint represents. For example, a metric type can be gauge,
counter, cumulative counter, or histogram. A metric type helps Splunk Observability Cloud
to interpret and display the data correctly1.
Question # 7
| What are the best practices for creating detectors? (select all that apply) |
| A. View data at highest resolution.
| | B. Have a consistent value.
| | C. View detector in a chart.
| | D. Have a consistent type of measurement. |
A. View data at highest resolution.
B. Have a consistent value.
C. View detector in a chart.
D. Have a consistent type of measurement.
Explanation: The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or
patterns in the data that could indicate anomalies or issues1.
Have a consistent value. This means that the metric or dimension used for
detection should have a clear and stable meaning across different sources,
contexts, and time periods. For example, avoid using metrics that are affected by
changes in configuration, sampling, or aggregation2.
View detector in a chart. This helps to visualize the data and the detector logic, as
well as to identify any false positives or negatives. It also allows to adjust the
detector parameters and thresholds based on the data distribution and behavior3.
Have a consistent type of measurement. This means that the metric or dimension
used for detection should have the same unit and scale across different sources,
contexts, and time periods. For example, avoid mixing bytes and bits, or seconds
and milliseconds.
Question # 8
| The Sum Aggregation option for analytic functions does which of the following? |
| A. Calculates the number of MTS present in the plot.
| | B. Calculates 1/2 of the values present in the input time series.
| | C. Calculates the sum of values present in the input time series across the entire
environment or per group.
| | D. Calculates the sum of values per time series across a period of time. |
C. Calculates the sum of values present in the input time series across the entire
environment or per group.
Explanation:
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the
metrics concepts that is covered in the exam is analytic functions. Analytic functions are
mathematical operations that can be applied to metrics to transform, aggregate, or analyze
them.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the
recommended courses for preparing for the exam is Introduction to Splunk Infrastructure
Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Analytic
Functions, which explains that analytic functions can be used to perform calculations on
metrics, such as sum, average, min, max, count, etc. The document also provides
examples of how to use analytic functions in charts and dashboards.
One of the analytic functions that can be used is Sum Aggregation, which calculates the
sum of values present in the input time series across the entire environment or per group.
The document gives an example of how to use Sum Aggregation to calculate the total CPU
usage across all hosts in a group by using the following syntax:
sum(cpu.utilization) by hostgroup
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Splunk O11y Cloud Certified Metrics User Exam Exam Dumps
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