Splunk SPLK-4001 Online Practice Questions and Exam Preparation

Splunk SPLK-4001 Online Questions & Answers

• Question 1:

  With exceptions for transformations or timeshifts, at what resolution do detectors operate?

  A. 10 seconds
  B. The resolution of the chart
  C. The resolution of the dashboard
  D. Native resolution
  D. Native resolution

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  Explanation/Reference:

  According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.

• Question 2:

  To smooth a very spiky cpu.utilization metric, what is the correct analytic function to better see if the cpu. utilization for servers is trending up over time?

  A. Rate/Sec
  B. Median
  C. Mean (by host)
  D. Mean (Transformation)
  D. Mean (Transformation)

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  Explanation/Reference:

  The correct answer is D. Mean (Transformation).

  According to the web search results, a mean transformation is an analytic function that returns the average value of a metric or a dimension over a specified time interval. A mean transformation can be used to smooth a very spiky metric, such as cpu.utilization, by reducing the impact of outliers and noise. A mean transformation can also help to see if the metric is trending up or down over time, by showing the general direction of the average value. For example, to smooth the cpu.utilization metric and see if it is trending up over time, you can use the following SignalFlow code: mean(1h, counters("cpu.utilization")) This will return the average value of the cpu.utilization counter metric for each metric time series (MTS) over the last hour. You can then use a chart to visualize the results and compare the mean values across different MTS. Option A is incorrect because rate/sec is not an analytic function, but rather a rollup function that returns the rate of change of data points in the MTS reporting interval1. Rate/sec can be used to convert cumulative counter metrics into counter metrics, but it does not smooth or trend a metric. Option B is incorrect because median is not an analytic function, but rather an aggregation function that returns the middle value of a metric or a dimension over the entire time range1. Median can be used to find the typical value of a metric, but it does not smooth or trend a metric. Option C is incorrect because mean (by host) is not an analytic function, but rather an aggregation function that returns the average value of a metric or a dimension across all MTS with the same host dimension1. Mean (by host) can be used to compare the performance of different hosts, but it does not smooth or trend a metric. Mean (Transformation) is an analytic function that allows you to smooth a very spiky metric by applying a moving average over a specified time window. This can help you see the general trend of the metric over time, without being distracted by the short-term fluctuations To use Mean (Transformation) on a cpu.utilization metric, you need to select the metric from the Metric Finder, then click on Add Analytics and choose Mean (Transformation) from the list of functions. You can then specify the time window for the moving average, such as 5 minutes, 15 minutes, or 1 hour. You can also group the metric by host or any other dimension to compare the smoothed values across different servers2 To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation https://docs.splunk.com/Observability/gdi/metrics/analytics.html

• Question 3:

  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
  A. Max Delay

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  Explanation/Reference:

  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 4:

  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?

  A. Info, Warning, Minor, Major, and Emergency.
  B. Debug, Warning, Minor, Major, and Critical.
  C. Info, Warning, Minor, Major, and Critical.
  D. Info, Warning, Minor, Severe, and Critical.
  C. Info, Warning, Minor, Major, and Critical.

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  Explanation/Reference:

  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 alert 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 documentations. https://docs.splunk.com/Observability/alerts-detectors- notifications/detectors.html#Create-a-standalone-detector https://docs.splunk.com/Observability/alerts-detectors-notifications/detector- options.html#Severity-levels

• Question 5:

  Which of the following are correct ports for the specified components in the OpenTelemetry Collector?

  A. gRPC (4000), SignalFx (9943), Fluentd (6060)
  B. gRPC (6831), SignalFx (4317), Fluentd (9080)
  C. gRPC (4459), SignalFx (9166), Fluentd (8956)
  D. gRPC (4317), SignalFx (9080), Fluentd (8006)
  D. gRPC (4317), SignalFx (9080), Fluentd (8006)

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  Explanation/Reference:

  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 result. You can also see the agent and gateway configuration files in the same result for more details. https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

• Question 6:

  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.
  A. Custom events that have been sent in from an external source.
  B. Events created when a detector clears an alert.
  D. Events created when a detector triggers an alert.

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  Explanation/Reference:

  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 7:

  When installing OpenTelemetry Collector, which error message is indicative that there is a misconfigured realm or access token?

  A. 403 (NOT ALLOWED)
  B. 404 (NOT FOUND)
  C. 401 (UNAUTHORIZED)
  D. 503 (SERVICE UNREACHABLE)
  C. 401 (UNAUTHORIZED)

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  Explanation/Reference:

  The correct answer is C. 401 (UNAUTHORIZED).

  According to the web search results, a 401 (UNAUTHORIZED) error message is indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector1. A 401 (UNAUTHORIZED) error message means that the request was not authorized by the server due to invalid credentials. A realm is a parameter that specifies the scope of protection for a resource, such as a Splunk Observability Cloud endpoint. An access token is a credential that grants access to a resource, such as a Splunk Observability Cloud API. If the realm or the access token is misconfigured, the request to install OpenTelemetry Collector will be rejected by the server with a 401 (UNAUTHORIZED) error message. Option A is incorrect because a 403 (NOT ALLOWED) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 403 (NOT ALLOWED) error message means that the request was authorized by the server but not allowed due to insufficient permissions. Option B is incorrect because a 404 (NOT FOUND) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 404 (NOT FOUND) error message means that the request was not found by the server due to an invalid URL or resource. Option D is incorrect because a 503 (SERVICE UNREACHABLE) error message is not indicative that there is a misconfigured realm or access token when installing OpenTelemetry Collector. A 503 (SERVICE UNREACHABLE) error message means that the server was unable to handle the request due to temporary overload or maintenance.

• Question 8:

  What is the limit on the number of properties that an MTS can have?

  A. 64
  B. 36
  C. No limit
  D. 50
  A. 64

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  Explanation/Reference:

  The correct answer is A. 64. 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 362 To learn more about how to use properties and dimensions in Splunk Observability Cloud, you can refer to this documentation. https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions- mts.html#Custom-properties https://docs.splunk.com/Observability/metrics-and- metadata/metrics-dimensions-mts.html

• Question 9:

  A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.

  How does the engineer see if the new release lowered average memory utilization?

  A. On the chart for plot A, select Add Analytics, then select MeanrTransformation. In the window that appears, select 'version' from the Group By field.
  B. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-l'.
  C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.
  D. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version1.
  C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.

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  Explanation/Reference:

  The correct answer is C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select `version' from the Group By field.

  This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the `canary' and `stable' versions to see if there is a significant difference. To

  learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation1.

  1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html

• Question 10:

  A user wants to add a link to an existing dashboard from an alert. When they click the dimension value in the alert message, they are taken to the dashboard keeping the context. How can this be accomplished? (select all that apply)

  A. Build a global data link.
  B. Add a link to the Runbook URL.
  C. Add a link to the field.
  D. Add the link to the alert message body.
  A. Build a global data link.
  C. Add a link to the field.

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  Explanation/Reference:

  The possible ways to add a link to an existing dashboard from an alert are: Build a global data link. A global data link is a feature that allows you to create a link from any dimension value in any chart or table to a dashboard of your choice. You can specify the source and target dashboards, the dimension name and value, and the query parameters to pass along. When you click on the dimension value in the alert message, you will be taken to the dashboard with the context preserved Add a link to the field. A field link is a feature that allows you to create a link from any field value in any search result or alert message to a dashboard of your choice. You can specify the field name and value, the dashboard name and ID, and the query parameters to pass along. When you click on the field value in the alert message, you will be taken to the dashboard with the context preserved Therefore, the correct answer is A and C. To learn more about how to use global data links and field links in Splunk Observability Cloud, you can refer to these documentations. https://docs.splunk.com/Observability/gdi/metrics/charts.html#Global-data-links https://docs.splunk.com/Observability/gdi/metrics/search.html#Field-links