UPDATED [2023] Pass Splunk SPLK-4001 Exam in First Attempt Guaranteed [Q13-Q35]

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UPDATED [2023] Pass Splunk SPLK-4001 Exam in First Attempt Guaranteed

Pass SPLK-4001 Exam Latest Practice Questions

The SPLK-4001 exam is aimed at professionals who work with Splunk’s cloud-based metrics offerings. SPLK-4001 exam is designed to test a candidate’s knowledge of metrics collection, analysis, and visualization using Splunk Cloud. SPLK-4001 exam covers a broad range of topics, including the fundamentals of metrics, the Splunk Metrics Data Model, the Splunk Metrics Store, and advanced metrics analysis and visualization techniques.

Splunk SPLK-4001 exam is a challenging exam that requires a significant amount of preparation and study. Candidates must have a strong understanding of the Splunk platform, as well as a deep knowledge of cloud-based metrics and monitoring.

Q13. Which of the following are true about organization metrics? (select all that apply)

Organization metrics give insights into system usage, system limits, data ingested and token quotas.

Organization metrics count towards custom MTS limits.

Organization metrics are included for free.

A user can plot and alert on them like metrics they send to Splunk Observability Cloud.

Q14. To refine a search for a metric a customer types host: test-*. What does this filter return?

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

Error

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

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

Q15. For which types of charts can individual plot visualization be set?

Line, Bar, Column

Bar, Area, Column

Line, Area, Column

Histogram, Line, Column

Q16. 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?

Info, Warning, Minor, Major, and Emergency.

Debug, Warning, Minor, Major, and Critical.

Info, Warning, Minor, Major, and Critical.

Info, Warning, Minor, Severe, and Critical.

Q17. What information is needed to create a detector?

Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients

Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients

Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients

Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients

Q18. Which of the following are ways to reduce flapping of a detector? (select all that apply)

Configure a duration or percent of duration for the alert.

Establish a reset threshold for the detector.

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

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

Q19. Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?

/opt/splunk/

/etc/otel/collector/

/etc/opentelemetry/

/etc/system/default/

Q20. Which analytic function can be used to discover peak page visits for a site over the last day?

Maximum: Transformation (24h)

Maximum: Aggregation (Id)

Lag: (24h)

Count: (Id)

Q21. Which of the following are supported rollup functions in Splunk Observability Cloud?

average, latest, lag, min, max, sum, rate

std_dev, mean, median, mode, min, max

sigma, epsilon, pi, omega, beta, tau

1min, 5min, 10min, 15min, 30min

Q22. 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?

Max Delay

Duration

Latency

Extrapolation Policy

Q23. Which of the following is optional, but highly recommended to include in a datapoint?

Metric name

Timestamp

Value

Metric type

Q24. A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?

The detector has an incorrect alert rule.

The detector has an incorrect signal,

The detector is disabled.

The detector has a muting rule.

Q25. An SRE creates a new detector to receive an alert when server latency is higher than 260 milliseconds.
Latency below 260 milliseconds is healthy for their service. The SRE creates a New Detector with a Custom Metrics Alert Rule for latency and sets a Static Threshold alert condition at 260ms.
How can the number of alerts be reduced?

Adjust the threshold.

Adjust the Trigger sensitivity. Duration set to 1 minute.

Adjust the notification sensitivity. Duration set to 1 minute.

Choose another signal.

Q26. The alert recipients tab specifies where notification messages should be sent when alerts are triggered or cleared. Which of the below options can be used? (select all that apply)

Invoke a webhook URL.

Export to CSV.

Send an SMS message.

Send to email addresses.

Q27. What happens when the limit of allowed dimensions is exceeded for an MTS?

The additional dimensions are dropped.

The datapoint is averaged.

The datapoint is updated.

The datapoint is dropped.

Q28. Which of the following are accurate reasons to clone a detector? (select all that apply)

To modify the rules without affecting the existing detector.

To reduce the amount of billed TAPM for the detector.

To add an additional recipient to the detector’s alerts.

To explore how a detector was created without risk of changing it.

Q29. Clicking a metric name from the results in metric finder displays the metric in Chart Builder. What action needs to be taken in order to save the chart created in the UI?

Create a new dashboard and save the chart.

Save the chart to multiple dashboards.

Make sure that data is coming in for the metric then save the chart.

Save the chart to a dashboard.

Q30. What constitutes a single metrics time series (MTS)?

A series of timestamps that all reflect the same metric.

A set of data points that all have the same metric name and list of dimensions.

A set of data points that use different dimensions but the same metric name.

A set of metrics that are ordered in series based on timestamp.

Q31. 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)

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

Events created when a detector clears an alert.

Random alerts from active detectors.

Events created when a detector triggers an alert.

Q32. Which of the following chart visualization types are unaffected by changing the time picker on a dashboard?
(select all that apply)

Single Value

Heatmap

Line

List

Q33. 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?

Rate/Sec

Median

Mean (by host)

Mean (Transformation)

Explanation
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 interval1. 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 fluctuations1 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 documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html

Q34. Which of the following statements about adding properties to MTS are true? (select all that apply)

Properties can be set via the API.

Properties are sent in with datapoints.

Properties are applied to dimension key:value pairs and propagated to all MTS with that dimension

Properties can be set in the UI under Metric Metadata.

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