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| Section | Weight | Objectives |
|---|---|---|
| Metrics Concepts | 15% | - List the components of a datapoint - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Data resolution, rollups |
| Introduction to Metric Visualization | 15% | - Analyze data in charts - Look for metrics - Use rollups and analytical tools correctly - Differentiate between several chart visualization types - Create charts and dashboards - Create widgets and showcase groups - Visualize a measure in a chart |
| Monitor Using Built-in Content | 10% | - Use the Cluster Analyzer to pinpoint the root of some problems - Subscribe to alerts - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Interact with data using built-in content |
| Create Efficient Dashboards and Alerts | 10% | - Configure personal information linkages - Include instructions on interfaces - View dashboard happenings - Create single-instance dashboard panels |
| Intro to Alerting on Metrics using Detectors | 10% | - Clone an existing detector - Make a solo detector - Make a muting rule - Make the detector from a chart |
| Get Metrics In with OpenTelemetry | 10% | - Deploy the OTel Collector on Linux - Configure the OTel Collector - Troubleshooting common errors - General OpenTelemetry Concepts - Edit the configuration |
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NEW QUESTION # 39
Which of the following statements are true about local data links? (select all that apply)
Answer: B,D
Explanation:
Explanation
The correct answers are A and D.
According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs.
The document explains that there are two types of data links: global and local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:
Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log.
Only Splunk Observability Cloud administrators can delete local data links.
Therefore, based on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:
B is false because local data links can have an external destination as well as an internal one.
C is false because anyone with write permission for a dashboard can create local data links, not just administrators.
NEW QUESTION # 40
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: A
Explanation:
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 To learn more about how to use processors in the OpenTelemetry Collector, you can refer to this documentation1.
1: https://opentelemetry.io/docs/collector/configuration/#processors 2:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3:
https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor
NEW QUESTION # 41
When configuring a new receiver to send data to Splunk Observability Cloud, which sections must the new receiver be added to in the OpenTelemetry Collector configuration file? (Choose all that apply.)
Answer: A,C,D
Explanation:
In the OpenTelemetry Collector configuration, a new receiver must be defined under receivers, referenced in a pipeline, and paired with an appropriate exporter to send data to Splunk Observability Cloud.
NEW QUESTION # 42
Changes to which type of metadata result in a new metric time series?
Answer: A
Explanation:
The correct answer is A. Dimensions.
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS)1 Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host2, you will create a new MTS for the same metric name1 Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed2 To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/metrics-and-metadata/metrics.html#Dimensions 2: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html
NEW QUESTION # 43
A customer has a very dynamic infrastructure. During every deployment, all existing instances are destroyed, and new ones are created Given this deployment model, how should a detector be created that will not send false notifications of instances being down?
Answer: C
Explanation:
According to the web search results, ephemeral infrastructure is a term that describes instances that are auto-scaled up or down, or are brought up with new code versions and discarded or recycled when the next code version is deployed. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down. To use this feature, you need to do the following steps:
Create the detector as usual, by selecting the metric or dimension that you want to monitor and alert on, and choosing the alert condition and severity level. Select Alert settings, then select Ephemeral Infrastructure. This will enable a special mode for the detector that will automatically clear alerts for instances that are expected to be terminated. Enter the expected lifetime of an instance in minutes. This is the maximum amount of time that an instance is expected to live before being replaced by a new one. For example, if your instances are replaced every hour, you can enter 60 minutes as the expected lifetime.
Save the detector and activate it.
With this feature, the detector will only trigger alerts when an instance stops reporting a metric unexpectedly, based on its expected lifetime. If an instance stops reporting a metric within its expected lifetime, the detector will assume that it was terminated on purpose and will not trigger an alert.
NEW QUESTION # 44
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