Question # 1
What happens when the limit of allowed dimensions is exceeded for an MTS?
| A. The additional dimensions are dropped.
| B. The datapoint is averaged.
| C. The datapoint is updated.
| D. The datapoint is dropped.
|
A. The additional dimensions are dropped.
Explanation:
According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS1. Splunk Observability Cloud has a limit of 36 unique dimensions per MTS2. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud2. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.
Question # 2
| An SRE creates an event feed chart in a dashboard that shows a list of events that meet
criteria they specify. Which of the following should they include? (select all that apply) | | A. Custom events that have been sent in from an external source.
| | B. Events created when a detector clears an alert.
| | C. Random alerts from active detectors.
| | D. Events created when a detector triggers an alert. |
A. Custom events that have been sent in from an external source.
B. Events created when a detector clears an alert.
D. Events created when a detector triggers an alert.
Explanation:
According to the web search results1, an event feed chart is a type of chart that shows a
list of events that meet criteria you specify. An event feed chart can display one or more
event types depending on how you specify the criteria. The event types that you can
include in an event feed chart are:
Custom events that have been sent in from an external source: These are events
that you have created or received from a third-party service or tool, such as AWS
CloudWatch, GitHub, Jenkins, or PagerDuty. You can send custom events to
Splunk Observability Cloud using the API or the Event Ingest Service.
Events created when a detector triggers or clears an alert: These are events that
are automatically generated by Splunk Observability Cloud when a detector
evaluates a metric or dimension and finds that it meets the alert condition or
returns to normal. You can create detectors to monitor and alert on various metrics
and dimensions using the UI or the API.
Therefore, option A, B, and D are correct.
Question # 3
| What is the limit on the number of properties that an MTS can have? | | A. 64 | | B. 36 | | C. No limit | | D. 50 |
A. 64
Explanation: The correct answer is A. 64.
According to the web search results, the limit on the number of properties that an MTS can
have is 64. A property is a key-value pair that you can assign to a dimension of an existing
MTS to add more context to the metrics. For example, you can add the property use: QA to
the host dimension of your metrics to indicate that the host is used for QA1
Properties are different from dimensions, which are key-value pairs that are sent along with
the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify
an MTS. The limit on the number of dimensions per MTS is 362
To learn more about how to use properties and dimensions in Splunk Observability Cloud,
you can refer to this documentation2.
Question # 4
| An SRE came across an existing detector that is a good starting point for a detector they
want to create. They clone the detector, update the metric, and add multiple new signals.
As a result of the cloned detector, which of the following is true? | | A. The new signals will be reflected in the original detector.
| | B. The new signals will be reflected in the original chart.
| | C. You can only monitor one of the new signals.
| | D. The new signals will not be added to the original detector. |
D. The new signals will not be added to the original detector.
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a
detector creates a copy of the detector that you can modify without affecting the original
detector. You can change the metric, filter, and signal settings of the cloned detector.
However, the new signals that you add to the cloned detector will not be reflected in the
original detector, nor in the original chart that the detector was based on. Therefore, option
D is correct.
Option A is incorrect because the new signals will not be reflected in the original detector.
Option B is incorrect because the new signals will not be reflected in the original chart.
Option C is incorrect because you can monitor all of the new signals that you add to the
cloned detector.
Question # 5
| How is it possible to create a dashboard group that no one else can edit? | | A. Ask the admin to lock the dashboard group.
| | B. Restrict the write access on the dashboard group.
| | C. Link the dashboard group to the team.
| | D. Hide the edit menu on the dashboard group. |
B. Restrict the write access on the dashboard group.
Explanation:
According to the web search results, dashboard groups are a feature of Splunk
Observability Cloud that allows you to organize and share dashboards with other users in
your organization1. You can set permissions for each dashboard group, such as who can
view, edit, or manage the dashboards in the group1. To create a dashboard group that no
one else can edit, you need to do the following steps:
-
Create a dashboard group as usual, by selecting Dashboard Group from the
Create menu on the navigation bar, entering a name and description, and adding
dashboards to the group1.
-
Select Alert settings from the Dashboard actions menu () on the top right corner of
the dashboard group. This will open a dialog box where you can configure the
permissions for the dashboard group1.
-
Under Write access, select Only me. This will restrict the write access to the
dashboard group to yourself only. No one else will be able to edit or delete the
dashboards in the group1.
-
Click Save. This will create a dashboard group that no one else can edit.
Question # 6
For a high-resolution metric, what is the highest possible native resolution of the metric?
| A. 2 seconds
| B. 15 seconds
| C. 1 second
| D. 5 seconds
|
C. 1 second
Explanation:
The correct answer is C. 1 second.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.
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 Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.
Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.
Question # 7
| Which of the following are accurate reasons to clone a detector? (select all that apply) | | A. To modify the rules without affecting the existing detector.
| | B. To reduce the amount of billed TAPM for the detector.
| | C. To add an additional recipient to the detector's alerts.
| | D. To explore how a detector was created without risk of changing it. |
A. To modify the rules without affecting the existing detector.
D. To explore how a detector was created without risk of changing it.
Explanation: The correct answers are A and D.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the
alerting concepts that is covered in the exam is detectors and alerts. Detectors are the
objects that define the conditions for generating alerts, and alerts are the notifications that
are sent when those conditions are met.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the
recommended courses for preparing for the exam is Alerting with Detectors, which covers
how to create, modify, and manage detectors and alerts.
In the Alerting with Detectors course, there is a section on Cloning Detectors, which
explains that cloning a detector creates a copy of the detector with all its settings, rules,
and alert recipients. The document also provides some reasons why you might want to
clone a detector, such as:
To modify the rules without affecting the existing detector. This can be useful if you
want to test different thresholds or conditions before applying them to the original
detector.
To explore how a detector was created without risk of changing it. This can be
helpful if you want to learn from an existing detector or use it as a template for
creating a new one.
Therefore, based on these documents, we can conclude that A and D are accurate reasons
to clone a detector. B and C are not valid reasons because:
Cloning a detector does not reduce the amount of billed TAPM for the detector.
TAPM stands for Tracked Active Problem Metric, which is a metric that has been
alerted on by a detector. Cloning a detector does not change the number of TAPM
that are generated by the original detector or the clone.
Cloning a detector does not add an additional recipient to the detector’s alerts.
Cloning a detector copies the alert recipients from the original detector, but it does
not add any new ones. To add an additional recipient to a detector’s alerts, you
need to edit the alert settings of the detector.
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