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| Section | Weight | Objectives |
|---|---|---|
| Introduction to Metric Visualization | 15% | - Analyze data in charts - Visualize a measure in a chart - Differentiate between several chart visualization types - Use rollups and analytical tools correctly - Create widgets and showcase groups - Look for metrics - Create charts and dashboards |
| Intro to Alerting on Metrics using Detectors | 10% | - Make a solo detector - Make the detector from a chart - Make a muting rule - Clone an existing detector |
| Get Metrics In with OpenTelemetry | 10% | - General OpenTelemetry Concepts - Deploy the OTel Collector on Linux - Troubleshooting common errors - Edit the configuration - Configure the OTel Collector |
| 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 |
| Create Efficient Dashboards and Alerts | 10% | - Configure personal information linkages - View dashboard happenings - Include instructions on interfaces - Create single-instance dashboard panels |
| Monitor Using Built-in Content | 10% | - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems - Interact with data using built-in content - Subscribe to alerts - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution |
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NEW QUESTION # 10
The built-in Kubernetes Navigator includes which of the following?
Answer: D
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 # 11
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?
Answer: B
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 # 12
What is the limit on the number of properties that an MTS can have?
Answer: A
Explanation:
According to the web search results, the limit on the number of properties that an MTS can have is 64. A property is a key-value pair that you can assign to a dimension of an existing MTS to add more context to the metrics. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host is used for QA.
Properties are different from dimensions, which are key-value pairs that are sent along with the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify an MTS.
The limit on the number of dimensions per MTS is 36.
NEW QUESTION # 13
A customer is sending data from a machine that is over-utilized. Because of a lack of system resources, datapoints from this machine are often delayed by up to 10 minutes. Which setting can be modified in a detector to prevent alerts from firing before the datapoints arrive?
Answer: A
Explanation:
Explanation
The correct answer is A. Max Delay.
Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points1 In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points arrive, and avoid false positives or missing data1 To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector-options.html#Max-Delay
NEW QUESTION # 14
Which action allows you to analyze data across different dimensions and perform calculations based on a set of rows?
Answer: D
NEW QUESTION # 15
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