Splunk Splunk Certifications SPLK-4001 Questions & Answers

• Question 31:

  Which of the following are ways to reduce flapping of a detector? (select all that apply)

  A. Configure a duration or percent of duration for the alert.

  B. Establish a reset threshold for the detector.

  C. Enable the anti-flap setting in the detector options menu.

  D. Apply a smoothing transformation (like a rolling mean) to the input data for the detector.

  Correct Answer: AD

  According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the

  threshold value. To reduce flapping, the article suggests the following ways:

  Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and

  focus on more persistent issues.

  Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the

  impact of a single extreme observation and make the signal less fluctuating.

• Question 32:

  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. Select the Sharded option when creating the plot.

  B. Add a filter to narrow the scope of the measurement.

  C. Add a restricted scope adjustment to the plot.

  D. When creating the plot, add a discriminator.

  Correct Answer: B

  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 it 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 Support To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap https://docs.splunk.com/Observability/gdi/metrics/search.html

• Question 33:

  An SRE creates an event feed chart in a dashboard that shows a list of events that meet criteria they specify. Which of the following should they include? (select all that apply)

  A. Custom events that have been sent in from an external source.

  B. Events created when a detector clears an alert.

  C. Random alerts from active detectors.

  D. Events created when a detector triggers an alert.

  Correct Answer: ABD

  According to the web search results, an event feed chart is a type of chart that shows a list of events that meet criteria you specify. An event feed chart can display one or more event types depending on how you specify the criteria. The event

  types that you can include in an event feed chart are:

  Custom events that have been sent in from an external source: These are events that you have created or received from a third-party service or tool, such as AWS CloudWatch, GitHub, Jenkins, or PagerDuty. You can send custom events to

  Splunk Observability Cloud using the API or the Event Ingest Service. Events created when a detector triggers or clears an alert: These are events that are automatically generated by Splunk Observability Cloud when a detector evaluates a

  metric or dimension and finds that it meets the alert condition or returns to normal. You can create detectors to monitor and alert on various metrics and dimensions using the UI or the API.

  Therefore, option A, B, and D are correct.

• Question 34:

  To refine a search for a metric a customer types host: test-*. What does this filter return?

  A. Only metrics with a dimension of host and a value beginning with test-.

  B. Error

  C. Every metric except those with a dimension of host and a value equal to test.

  D. Only metrics with a value of test- beginning with host.

  Correct Answer: A

  The correct answer is A. Only metrics with a dimension of host and a value beginning with test-. This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics https://docs.splunk.com/Observability/gdi/metrics/search.html

• Question 35:

  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?

  A. Max Delay

  B. Duration

  C. Latency

  D. Extrapolation Policy

  Correct Answer: A

  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 data

  To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation.

  1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector- options.html#Max-Delay

• Question 36:

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

  A. Maximum / Minimum

  B. Best/Worst

  C. Exclude / Include

  D. Top / Bottom

  Correct Answer: D

  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 period To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation.

• Question 37:

  The built-in Kubernetes Navigator includes which of the following?

  A. Map, Nodes, Workloads, Node Detail, Workload Detail, Group Detail, Container Detail

  B. Map, Nodes, Processors, 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, Pod Detail, Container Detail

  Correct Answer: D

  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 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 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 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 To learn more about how to use Kubernetes Navigator in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Kubernetes- Navigator: https://docs.splunk.com/observability/infrastructure/monitor/k8s- nav.html#Detail-pages https://docs.splunk.com/observability/infrastructure/monitor/k8s- nav.html

• Question 38:

  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. Single-instance dashboard

  B. Machine dashboard

  C. Multiple-service dashboard

  D. Server dashboard

  Correct Answer: A

  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.

• Question 39:

  The Sum Aggregation option for analytic functions does which of the following?

  A. Calculates the number of MTS present in the plot.

  B. Calculates 1/2 of the values present in the input time series.

  C. Calculates the sum of values present in the input time series across the entire environment or per group.

  D. Calculates the sum of values per time series across a period of time.

  Correct Answer: C

  According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is analytic functions. Analytic functions are mathematical operations that can be applied to metrics to transform, aggregate, or analyze them. The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization. In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Analytic Functions, which explains that analytic functions can be used to perform calculations on metrics, such as sum, average, min, max, count, etc. The document also provides examples of how to use analytic functions in charts and dashboards. One of the analytic functions that can be used is Sum Aggregation, which calculates the sum of values present in the input time series across the entire environment or per group. The document gives an example of how to use Sum Aggregation to calculate the total CPU usage across all hosts in a group by using the following syntax: sum(cpu.utilization) by hostgroup

• Question 40:

  Which of the following statements are true about local data links? (select all that apply)

  A. Anyone with write permission for a dashboard can add local data links that appear on that dashboard.

  B. Local data links can only have a Splunk Observability Cloud internal destination.

  C. Only Splunk Observability Cloud administrators can create local links.

  D. Local data links are available on only one dashboard.

  Correct Answer: AD

  The correct answers are A and D.

  According to the Get started with Splunk Observability Cloud document1, 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. Therefore, based

  on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:

  B is false because local data links can have an external destination as well as an internal one.

  C is false because anyone with write permission for a dashboard can create local data links, not just administrators.