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SPLK-4001 Practice Questions

Question # 1
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)
A. Single Value
B. Heatmap
C. Line
D. List


A. Single Value
D. List

Explanation: The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1.
List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2.
Therefore, the correct answer is A and D.

Question # 2
Changes to which type of metadata result in a new metric time series?
A. Dimensions
B. Properties
C. Sources
D. Tags


A. Dimensions

Explanation: The correct answer is A. Dimensions.
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS)1.
Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host2, you will create a new MTS for the same metric name1.
Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed2.
To learn more about how to use metadata in Splunk Observability Cloud, you can refer to this documentation2.

Question # 3
What constitutes a single metrics time series (MTS)?
A. A series of timestamps that all reflect the same metric.
B. A set of data points that all have the same metric name and list of dimensions.
C. A set of data points that use different dimensions but the same metric name.
D. A set of metrics that are ordered in series based on timestamp.


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

Explanation: The correct answer is B. A set of data points that all have the same metric name and list of dimensions.
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension “hostname”: “host1” MTS2: Gauge metric cpu.utilization, dimension “hostname”: “host2” MTS3: Gauge metric memory.usage, dimension “hostname”: “host1”
A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp1.

Question # 4
For a high-resolution metric, what is the highest possible native resolution of the metric?
A. 2 seconds
B. 15 seconds
C. 1 second
D. 5 seconds


C. 1 second

Explanation:

The correct answer is C. 1 second.

According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.

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 Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1-second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.

Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.


Question # 5
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.


A. Configure a duration or percent of duration for the alert.
D. Apply a smoothing transformation (like a rolling mean) to the input data for the detector.

Explanation:
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 # 6
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

Explanation:

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 # 7
For which types of charts can individual plot visualization be set?
A. Line, Bar, Column
B. Bar, Area, Column
C. Line, Area, Column
D. Histogram, Line, Column


C. Line, Area, Column

Explanation: The correct answer is C. Line, Area, Column.
For line, area, and column charts, you can set the individual plot visualization to change the appearance of each plot in the chart. For example, you can change the color, shape, size, or style of the lines, areas, or columns. You can also change the rollup function, data resolution, or y-axis scale for each plot1.
To set the individual plot visualization for line, area, and column charts, you need to select the chart from the Metric Finder, then click on Plot Chart Options and choose Individual Plot Visualization from the list of options. You can then customize each plot according to your preferences2.

Question # 8
An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals. As a result of the cloned detector, which of the following is true?
A. The new signals will be reflected in the original detector.
B. The new signals will be reflected in the original chart.
C. You can only monitor one of the new signals.
D. The new signals will not be added to the original detector.


D. The new signals will not be added to the original detector.

Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a detector creates a copy of the detector that you can modify without affecting the original detector. You can change the metric, filter, and signal settings of the cloned detector.
However, the new signals that you add to the cloned detector will not be reflected in the original detector, nor in the original chart that the detector was based on. Therefore, option D is correct.
Option A is incorrect because the new signals will not be reflected in the original detector.
Option B is incorrect because the new signals will not be reflected in the original chart.
Option C is incorrect because you can monitor all of the new signals that you add to the cloned detector.

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Splunk SPLK-4001 Exam Dumps

Exam Name: Splunk O11y Cloud Certified Metrics User Exam
Certification Name: Splunk O11y Cloud Certified Metrics User

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  • Total Questions: 54
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