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100% Pass Splunk - Reliable SPLK-4001 - Training Splunk O11y Cloud Certified Metrics User Kit

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The Splunk SPLK-4001 exam covers a wide range of topics including the use of Splunk metrics, the configuration of metrics, the use of dashboards, and the creation of alerts. It also covers the use of the Splunk Cloud platform and the integration of metrics with other cloud-based platforms.

Splunk SPLK-4001 Exam Syllabus Topics:

Topic
Details

Topic 1

Correctly apply rollups and analytic functions
Apply analytic functions over moving and calendar time windows

Topic 2

Deploy the OTel Collector on Linux
Discriminate between types of metadata

Topic 3

Introduction to Alerting on Metrics with Detectors
Identify common issues with detectors

Topic 4

Create Efficient Dashboards and Alerts
Create dashboards and dashboard groups

Topic 5

Apply analytics functions to a subset of MTS in a signal
Troubleshooting common errors

Topic 6

General OpenTelemetry Concepts
Use the Cluster Analyzer to pinpoint the root of some problems

Topic 7

Create single-instance dashboards
Use built-in Kubernetes Dashboards to investigate and troubleshoot

Topic 8

View and alert on weekly, daily, or hourly comparisons
Create detectors to monitor populations

Topic 9

List the components of a datapoint
Use percentages and ratios to understand trends

The SPLK-4001 Exam is aimed at professionals who have a deep understanding of cloud infrastructure and are looking to expand their skills in metrics analysis and monitoring. Candidates should have prior experience working with Splunk and should be familiar with concepts such as data ingestion, dashboards, and alerts. Additionally, a solid grasp of programming languages such as Python or JavaScript is recommended.

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q38-Q43):

NEW QUESTION # 38
A customer deals with a holiday rush of traffic during November each year, but does not want to be flooded with alerts when this happens. The increase in traffic is expected and consistent each year. Which detector condition should be used when creating a detector for this data?

A. Historical Anomaly
B. Static Threshold
C. Calendar Window
D. Outlier Detection

Answer: A

Explanation:
historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern1. Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern1. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles1.
Historical anomaly is suitable for creating a detector for the customer's data, because it can account for the expected and consistent increase in traffic during November each year. Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly1. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation. However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly1.

NEW QUESTION # 39
Which of the following statements are true about local data links? (select all that apply)

A. Only Splunk Observability Cloud administrators can create local links.
B. Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
C. Local data links can only have a Splunk Observability Cloud internal destination.
D. Local data links are available on only one dashboard.

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
When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?

A. Info, Warning, Minor, Major, and Critical.
B. Info, Warning, Minor, Major, and Emergency.
C. Debug, Warning, Minor, Major, and Critical.
D. Info, Warning, Minor, Severe, and Critical.

Answer: A

Explanation:
Explanation
The correct answer is C. Info, Warning, Minor, Major, and Critical.
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1:
https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector
2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels

NEW QUESTION # 41
The Sum Aggregation option for analytic functions does which of the following?

A. Calculates the sum of values present in the input time series across the entire environment or per group.
B. Calculates the number of MTS present in the plot.
C. Calculates the sum of values per time series across a period of time.
D. Calculates 1/2 of the values present in the input time series.

Answer: A

Explanation:
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

NEW QUESTION # 42
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?

A. Check the Ephemeral checkbox when creating the detector.
B. Check the Dynamic checkbox when creating the detector.
C. Create the detector. Select Alert settings, then select Auto-Clear Alerts and enter an appropriate time period.
D. Create the detector. Select Alert settings, then select Ephemeral Infrastructure and enter the expected lifetime of an instance.

Answer: D

Explanation:
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 deployed1. Splunk Observability Cloud has a feature that allows you to create detectors for ephemeral infrastructure without sending false notifications of instances being down2. 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. Therefore, option B is correct.

NEW QUESTION # 43
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