Pass the Splunk Splunk O11y Cloud Certified Metrics User SPLK-4001 Questions and answers with CertsForce

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.

According to the Splunk Observability Cloud documentation1, lag is a rollup function that returns the difference between the most recent and the previous data point values seen in the metric time series reporting interval. This can be used to measure the time delta between a data point being sent and a data point being received, as long as the data points have timestamps that reflect their send and receive times. For example, if a data point is sent at 10:00:00 and received at 10:00:05, the lag value for that data point is 5 seconds.

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.

1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 2: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 3: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart : https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors

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.

The possible ways to add a link to an existing dashboard from an alert are:

Build a global data link. A global data link is a feature that allows you to create a link from any dimension value in any chart or table to a dashboard of your choice. You can specify the source and target dashboards, the dimension name and value, and the query parameters to pass along. When you click on the dimension value in the alert message, you will be taken to the dashboard with the context preserved1

Add a link to the field. A field link is a feature that allows you to create a link from any field value in any search result or alert message to a dashboard of your choice. You can specify the field name and value, the dashboard name and ID, and the query parameters to pass along. When you click on the field value in the alert message, you will be taken to the dashboard with the context preserved2

Therefore, the correct answer is A and C.

To learn more about how to use global data links and field links in Splunk Observability Cloud, you can refer to these documentations12.

1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Global-data-links 2: https://docs.splunk.com/Observability/gdi/metrics/search.html#Field-links

The correct answer is A, C, and D. Organization metrics give insights into system usage, system limits, data ingested and token quotas. Organization metrics are included for free. A user can plot and alert on them like metrics they send to Splunk Observability Cloud.

Organization metrics are a set of metrics that Splunk Observability Cloud provides to help you measure your organization’s usage of the platform. They include metrics such as:

Ingest metrics: Measure the data you’re sending to Infrastructure Monitoring, such as the number of data points you’ve sent.

App usage metrics: Measure your use of application features, such as the number of dashboards in your organization.

Integration metrics: Measure your use of cloud services integrated with your organization, such as the number of calls to the AWS CloudWatch API.

Resource metrics: Measure your use of resources that you can specify limits for, such as the number of custom metric time series (MTS) you’ve created1

Organization metrics are not charged and do not count against any system limits. You can view them in built-in charts on the Organization Overview page or in custom charts using the Metric Finder. You can also create alerts based on organization metrics to monitor your usage and performance1

To learn more about how to use organization metrics in Splunk Observability Cloud, you can refer to this documentation1.

1: https://docs.splunk.com/observability/admin/org-metrics.html