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Splunk SPLK-4001: Splunk O11y Cloud Certified Metrics User exam is an important certification for individuals who work with metrics data in the Splunk platform. It validates the skills and knowledge needed to work with metrics data in Splunk Cloud environments and is recognized globally by employers in the IT industry. By achieving this certification, individuals can demonstrate their expertise in using the Splunk platform for monitoring and analyzing metrics data, which can lead to new career opportunities and advancements in their current roles.
The Splunk SPLK-4001 Exam covers a range of topics, including understanding metrics, configuring and using cloud monitoring tools, designing dashboards and visualizations, and troubleshooting performance issues. SPLK-4001 exam is intended to assess your ability to work with Splunk in a cloud environment and to demonstrate your proficiency in using the tool to identify and resolve performance issues.
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To take the SPLK-4001 Exam, candidates must have a solid understanding of Splunk’s observability suite, as well as experience in monitoring cloud applications and infrastructure. SPLK-4001 exam consists of 60 multiple-choice questions, and candidates have 90 minutes to complete it. The passing score for the exam is 70%, and candidates who pass the exam will receive the Splunk O11y Cloud Certified Metrics User certification.
NEW QUESTION # 104
A team wants to visualize the 95th percentile of request latency across all instances of a service.
Which analytic function should be applied?
Answer: C
Explanation:
The Percentile analytic function allows users to calculate a specific percentile, such as p95, across the values of a signal, which is useful for understanding latency distribution and tail performance.
NEW QUESTION # 105
Which of the following statements is true of detectors created from a chart on a custom dashboard?
Answer: A
Explanation:
When you create a detector from a chart on a custom dashboard, the detector is automatically linked to the chart. This means that you can see the detector status and alerts on the chart, and you can access the detector settings from the chart menu. You can also unlink the detector from the chart if you want to.
Changes made to the chart do not affect the detector, and changes made to the detector do not affect the chart. The detector and the chart are independent entities that have their own settings and parameters. However, if you change the metric or dimension of the chart, you might lose the link to the detector.
The alerts generated by the detector will show up in the Alerts page, where you can view, manage, and acknowledge them. You can also see them on the team landing page if you assign the detector to a team.
NEW QUESTION # 106
A customer wants to build a single dashboard chart that overlays metrics from multiple different services, each with a different unit of measurement. Which visualization approach is most appropriate?
Answer: A
Explanation:
A dual-axis Chart allows two different value axes on the same chart, making it possible to meaningfully overlay signals with different units or scales without distorting either signal's visualization.
NEW QUESTION # 107
The built-in Kubernetes Navigator includes which of the following?
Answer: C
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 # 108
Which of the following is optional, but highly recommended to include in a datapoint?
Answer: A
Explanation:
A metric type is an optional, but highly recommended field that specifies the kind of measurement that a datapoint represents. For example, a metric type can be gauge, counter, cumulative counter, or histogram. A metric type helps Splunk Observability Cloud to interpret and display the data correctly.
NEW QUESTION # 109
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