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Splunk SPLK-4001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Collection & Ingestion15%- Integration with Kubernetes and cloud environments
- Signal flow and data processing pipeline
- OpenTelemetry configuration and deployment
Topic 2: Built-in Monitoring Content10%- Using pre-built dashboards and navigators
- Content packs and quick-start monitoring
Topic 3: Dashboards & Operational Efficiency10%- Reducing alert fatigue and optimizing monitoring
- Designing clear, performant dashboards
Topic 4: Analytics & Insights10%- Correlation and pattern detection
- Aggregation, filtering, and transformation functions
Topic 5: Visualizing Metrics20%- Visualization best practices and effective communication
- Using SignalFlow for custom queries
- Creating and configuring charts, graphs, and time-series
Topic 6: Alerting with Detectors15%- Notification rules and incident response
- Common use cases: latency, saturation, errors
- Threshold, anomaly, and multi-condition detectors
Topic 7: Metrics Concepts20%- Data retention and resolution settings
- Metric types: Gauges, Counters, Histograms, Summaries
- Dimensions, time-series data, and cardinality

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q20-Q25):

NEW QUESTION # 20
The built-in Kubernetes Navigator includes which of the following?

Answer: B

Explanation:
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 # 21
In the Splunk distribution of the OpenTelemetry Collector, what is the difference between the agent_config.yaml and the splunk-otel-collector.conf files?

Answer: B

Explanation:
In the Splunk distribution of the OpenTelemetry Collector, splunk-otel-collector.conf defines the OpenTelemetry pipeline, including receivers, processors, and exporters, while agent_config.yaml is used to configure Splunk-specific settings such as endpoint URLs and access tokens.


NEW QUESTION # 22
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?

Answer: A

Explanation:
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.


NEW QUESTION # 23
Olive is creating a dashboard for her team, and she wants to include instructions on the dashboard for what to do if a metric is outside of a desired range. Which element can she use to add these details to the dashboard?

Answer: C

Explanation:
A Text note element can be added to a dashboard to provide instructions, guidance, or context for users, such as steps to follow when a metric is outside the desired range.


NEW QUESTION # 24
For the mean analytic function, what steps are needed to create a time series where each point is the mean of values over the previous sliding window leading up to that point?

Answer: D

Explanation:
To create a time series with each point representing the mean over a sliding window, you must select the analytic function, apply it as a transformation, and specify the size of the time window.
This ensures that the mean is calculated over the defined interval leading up to each point.


NEW QUESTION # 25
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