SPLK-4001最新資料 & SPLK-4001学習教材あなたはSPLK-4001資格認定証明書を取得するためにSPLK-4001試験に合格しようとしていますか？ 私たちが知っているように、SPLK-4001資格認定証明書は高い給与、より良い職位などの利点があります。 おそらく、この時点では、私たちのSPLK-4001学習教材の助けが必要です。SPLK-4001学習教材は弊社の主力製品として、たくさんの受験者からいい評判をもらいました。 Splunk O11y Cloud Certified Metrics User 認定 SPLK-4001 試験問題 (Q30-Q35):質問 # 30
One server in a customer's data center is regularly restarting due to power supply issues. What type of dashboard could be used to view charts and create detectors for this server?
A. Server dashboard
B. Single-instance dashboard
C. Multiple-service dashboard
D. Machine dashboard
正解：B
解説：
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, a single-instance dashboard is a type of dashboard that displays charts and information for a single instance of a service or host. You can use a single-instance dashboard to monitor the performance and health of a specific server, such as the one that is restarting due to power supply issues. You can also create detectors for the metrics that are relevant to the server, such as CPU usage, memory usage, disk usage, and uptime. Therefore, option A is correct.
質問 # 31
Which component of the OpenTelemetry Collector allows for the modification of metadata?
A. Pipelines
B. Processors
C. Exporters
D. Receivers
正解：B
解説：
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-telemetr ... attributesprocessor 3: https://github.com/open-telemetr ... r/resourceprocessor
質問 # 32
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 Emergency.
B. Debug, Warning, Minor, Major, and Critical.
C. Info, Warning, Minor, Major, and Critical.
D. Info, Warning, Minor, Severe, and Critical.
正解：C
解説：
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/Observab ... standalone-detector
2: https://docs.splunk.com/Observab ... tml#Severity-levels
質問 # 33
When writing a detector with a large number of MTS, such as memory. free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of the choices below would most likely reduce the number of MTS below the plot cap?
A. Add a restricted scope adjustment to the plot.
B. When creating the plot, add a discriminator.
C. Select the Sharded option when creating the plot.
D. Add a filter to narrow the scope of the measurement.
正解：D
解説：
The correct answer is B. Add a filter to narrow the scope of the measurement.
A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included. For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1 Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observab ... html#Filter-metrics 2: https://docs.splunk.com/Observab ... ctors.html#Plot-cap 3: https://docs.splunk.com/Observability/gdi/metrics/search.html
質問 # 34
The built-in Kubernetes Navigator includes which of the following?
A. Map, Nodes, Processors, Node Detail, Workload Detail, Pod Detail, Container Detail
B. Map, Nodes, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail
C. Map, Clusters, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail
D. Map, Nodes, Workloads, Node Detail, Workload Detail, Group Detail, Container Detail
正解：B
解説：
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/observab ... ubernetes-Navigator 2: https://docs.splunk.com/observab ... v.html#Detail-pages 3: https://docs.splunk.com/observab ... onitor/k8s-nav.html