SPLK-4001在線考題，SPLK-4001測試引擎SPLK-4001 認證可代表豐富且多樣化的工作角色及責任。因此，取得特定的認證將可做為具備成功執行重要IT功能所需之能力的最佳證明。由於受到全世界企業專家的熱烈支持，SPLK-4001 認證仍是達到長期事業目標的最有效率的方法之一，並且是公司用來開發及留住重要IT人員的不二法門。但是如何在第一次嘗試中就能有效的通過Splunk 的 SPLK-4001 認證考試？這個問題的答案隨著 NewDumps 產生已經不再是問題了。 最新的 Splunk O11y Cloud Certified SPLK-4001 免費考試真題 (Q37-Q42):問題 #37
The built-in Kubernetes Navigator includes which of the following?
A. Map, Clusters, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail
B. Map, Nodes, Workloads, Node Detail, Workload Detail, Group Detail, Container Detail
C. Map, Nodes, Workloads, Node Detail, Workload Detail, Pod Detail, Container Detail
D. Map, Nodes, Processors, Node Detail, Workload Detail, Pod Detail, Container Detail
答案：C
解題說明：
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
問題 #38
What constitutes a single metrics time series (MTS)?
A. A series of timestamps that all reflect the same metric.
B. A set of metrics that are ordered in series based on timestamp.
C. A set of data points that all have the same metric name and list of dimensions.
D. A set of data points that use different dimensions but the same metric name.
答案：C
解題說明：
The correct answer is B. A set of data points that all have the same metric name and list of dimensions.
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1" MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2" MTS3: Gauge metric memory.usage, dimension "hostname": "host1" A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp1
問題 #39
What are the best practices for creating detectors? (select all that apply)
A. Have a consistent type of measurement.
B. Have a consistent value.
C. View detector in a chart.
D. View data at highest resolution.
答案：A,B,C,D
解題說明：
Explanation
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues1 Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation2 View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior3 Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
1: https://docs.splunk.com/Observab ... tices-for-detectors 2: https://docs.splunk.com/Observab ... tices-for-detectors 3: https://docs.splunk.com/Observab ... detector-in-a-chart : https://docs.splunk.com/Observab ... tices-for-detectors
問題 #40
Changes to which type of metadata result in a new metric time series?
A. Dimensions
B. Properties
C. Sources
D. Tags
答案：A
解題說明：
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/Observab ... ics.html#Dimensions 2: https://docs.splunk.com/Observab ... dimensions-mts.html
問題 #41
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?
A. gRPC (4317), SignalFx (9080), Fluentd (8006)
B. gRPC (6831), SignalFx (4317), Fluentd (9080)
C. gRPC (4459), SignalFx (9166), Fluentd (8956)
D. gRPC (4000), SignalFx (9943), Fluentd (6060)
答案：A
解題說明：
Explanation
The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006).
According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result1. You can also see the agent and gateway configuration files in the same result for more details.
1: https://docs.splunk.com/observab ... osed-endpoints.html