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Splunk SPLK-4001 (Splunk O11y Cloud Certified Metrics User) Exam is an industry-recognized certification program that helps to validate an individual’s knowledge and expertise in the area of cloud monitoring and analytics. Splunk O11y Cloud Certified Metrics User certification exam is specifically designed for Splunk Cloud administrators who are responsible for monitoring cloud-based infrastructure and applications. Through this certification program, candidates will gain technical expertise and knowledge required to implement and manage cloud monitoring strategies using Splunk Metrics.

To prepare for the SPLK-4001 Exam, individuals can take advantage of a variety of resources provided by Splunk. These include online courses, practice exams, and study guides. In addition, Splunk offers certification paths and badges for individuals who want to demonstrate their expertise in specific areas of Splunk.

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q19-Q24):

NEW QUESTION # 19
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. Debug, Warning, Minor, Major, and Critical.
B. Info, Warning, Minor, Major, and Emergency.
C. Info, Warning, Minor, Severe, and Critical.
D. Info, Warning, Minor, Major, and Critical.

Answer: D

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 # 20
The built-in Kubernetes Navigator includes which of the following?

A. Map, Nodes, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail
B. Map, Nodes, Workloads, Node Detail, Workload Detail, Group Detail, Container Detail
C. Map, Nodes, Processors, Node Detail, Workload Detail, Pod Detail, Container Detail
D. Map, Clusters, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail

Answer: A

Explanation:
Explanation
The correct answer is D. Map, Nodes, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail.
The built-in Kubernetes Navigator is a feature of Splunk Observability Cloud that provides a comprehensive and intuitive way to monitor the performance and health of Kubernetes environments. It includes the following views:
Map: A graphical representation of the Kubernetes cluster topology, showing the relationships and dependencies among nodes, pods, containers, and services. You can use the map to quickly identify and troubleshoot issues in your cluster1 Nodes: A tabular view of all the nodes in your cluster, showing key metrics such as CPU utilization, memory usage, disk usage, and network traffic. You can use the nodes view to compare and analyze the performance of different nodes1 Workloads: A tabular view of all the workloads in your cluster, showing key metrics such as CPU utilization, memory usage, network traffic, and error rate. You can use the workloads view to compare and analyze the performance of different workloads, such as deployments, stateful sets, daemon sets, or jobs1 Node Detail: A detailed view of a specific node in your cluster, showing key metrics and charts for CPU utilization, memory usage, disk usage, network traffic, and pod count. You can also see the list of pods running on the node and their status. You can use the node detail view to drill down into the performance of a single node2 Workload Detail: A detailed view of a specific workload in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and pod count. You can also see the list of pods belonging to the workload and their status. You can use the workload detail view to drill down into the performance of a single workload2 Pod Detail: A detailed view of a specific pod in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and container count. You can also see the list of containers within the pod and their status. You can use the pod detail view to drill down into the performance of a single pod2 Container Detail: A detailed view of a specific container in your cluster, showing key metrics and charts for CPU utilization, memory usage, network traffic, error rate, and log events. You can use the container detail view to drill down into the performance of a single container2 To learn more about how to use Kubernetes Navigator in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Kubernetes-Navigator 2:
https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Detail-pages 3:
https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html

NEW QUESTION # 21
Which analytic function can be used to discover peak page visits for a site over the last day?

A. Maximum: Transformation (24h)
B. Lag: (24h)
C. Maximum: Aggregation (Id)
D. Count: (Id)

Answer: A

Explanation:
According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:
maximum(24h, counters("page.visits"))
This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.

NEW QUESTION # 22
Which of the following statements about adding properties to MTS are true? (select all that apply)

A. Properties are sent in with datapoints.
B. Properties can be set in the UI under Metric Metadata.
C. Properties are applied to dimension key:value pairs and propagated to all MTS with that dimension
D. Properties can be set via the API.

Answer: B,D

Explanation:
According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud1. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA.
To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests2. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings3. Therefore, option A and D are correct.

NEW QUESTION # 23
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?

A. The detector has an incorrect alert rule.
B. The detector is disabled.
C. The detector has a muting rule.
D. The detector has an incorrect signal,

Answer: C

Explanation:
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.

NEW QUESTION # 24
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Splunk O11y Cloud Certified Metrics User Sample Questions (Q19-Q24):

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