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| Section | Weight | Objectives |
|---|---|---|
| Intro to Alerting on Metrics using Detectors | 10% | - Make the detector from a chart - Clone an existing detector - Make a muting rule - Make a solo detector |
| Introduction to Metric Visualization | 15% | - Create widgets and showcase groups - Use rollups and analytical tools correctly - Create charts and dashboards - Visualize a measure in a chart - Differentiate between several chart visualization types - Analyze data in charts - Look for metrics |
| Monitor Using Built-in Content | 10% | - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Interact with data using built-in content - Use the Cluster Analyzer to pinpoint the root of some problems - Subscribe to alerts - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution |
| Metrics Concepts | 15% | - Discriminate between types of metadata - Data resolution, rollups - List the components of a datapoint - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints |
| Get Metrics In with OpenTelemetry | 10% | - Edit the configuration - Troubleshooting common errors - Configure the OTel Collector - General OpenTelemetry Concepts - Deploy the OTel Collector on Linux |
| Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Configure personal information linkages - Include instructions on interfaces - Create single-instance dashboard panels |
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NEW QUESTION # 70
Which feature for troubleshooting Kubernetes performance at scale highlights instances that are in a bad state and producing theories about what those instances might have in common?
Answer: B
Explanation:
The Kubernetes Troubleshooter identifies instances that are in a degraded or bad state and suggests common factors or potential causes, helping users efficiently diagnose performance issues at scale.
NEW QUESTION # 71
Which of the following statements are true about local data links? (select all that apply)
Answer: C,D
Explanation:
According to the Get started with Splunk Observability Cloud document, 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.
NEW QUESTION # 72
The built-in Kubernetes Navigator includes which of the following?
Answer: C
Explanation:
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 cluster.
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 nodes.
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 jobs.
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 node.
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 workload.
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 pod.
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 container.
NEW QUESTION # 73
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?
Answer: B
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.
NEW QUESTION # 74
Which of the following statements about adding properties to MTS are true? (select all that apply)
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 Cloud. 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 requests. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings.
NEW QUESTION # 75
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