SPLK-1001 Practice Test 9 — 10 Questions

Question 1

Refer to the exhibit. The report returns 0 results even though there are error events in the data. What is the most likely issue?

Accepted Answer

The field name is 'error' but the data uses 'ERROR'. Splunk field names are case-sensitive. The search is looking for a field named 'error', but the CSV data contains a field named 'ERROR'. Since the field name does not match exactly, the `stats` command cannot find any events with the field 'error', resulting in zero results. Renaming the field in the search using `rename ERROR as error` or adjusting the field name in the data would resolve this.

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The stats command is misspelled

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The time range is not set

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The CSV file has no header

Question 2

An administrator needs to share a report with executives who prefer to see data in a tabular format with sorting capabilities. Which reporting method is best?

Accepted Answer

Table. A Table visualization in Splunk provides a tabular format with built-in sorting capabilities (clickable column headers), which directly meets the executives' requirement for viewing data in a structured, sortable table. Unlike other visualizations, the Table report type preserves raw event data in rows and columns, allowing users to reorder results by any field without additional configuration.

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Dashboard panel

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Gauges

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Pie chart

Question 3

When using an automatic lookup in props.conf, which setting controls the order in which multiple automatic lookups apply to the same sourcetype?

Accepted Answer

The numeric suffix after LOOKUP- in props.conf. In Splunk, when multiple automatic lookups are defined for the same sourcetype in props.conf, the order of execution is determined by the numeric suffix appended to the LOOKUP- stanza (e.g., LOOKUP-1, LOOKUP-2). Splunk processes these lookups in ascending numeric order, ensuring a predictable sequence. This allows administrators to chain lookups where the output of one lookup can feed into the next.

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The order in which they are listed in transforms.conf

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The `batch_index_query` setting

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The `priority` field in the lookup definition

Question 4

A user wants to add a drilldown to a dashboard panel so that clicking a value opens a related search in a new tab. Which Simple XML attribute is used?

Accepted Answer

drilldown. In Simple XML, the `drilldown` attribute is used to enable or disable click interactions on dashboard panels. When set to `drilldown`, clicking a value triggers a drilldown action, which by default opens a related search in the same tab. To open the search in a new tab, you must combine `drilldown` with the `target` attribute set to `_blank`. The `drilldown` attribute itself is the correct answer because it is the primary attribute that enables the drilldown behavior.

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link

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target

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href

Question 5

A user wants to add a trend indicator to a single value visualization showing whether the count increased or decreased compared to the previous period. Which feature should be used?

Accepted Answer

Comparison. The Comparison feature in a single value visualization allows you to show a trend indicator (e.g., an up or down arrow) that compares the current value to a previous time period, such as the previous week or month. This directly answers the user's need to see whether the count increased or decreased compared to the previous period. It is configured in the 'Single Value' visualization options under 'Comparison' settings.

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Trendline

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Color coding

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Sparkline

Question 6

Refer to the exhibit. What will be the output of this search?

Accepted Answer

The productId and its count for the 10 product IDs with the highest event counts. The search uses the `top` command, which by default returns the 10 most frequent values of the specified field (`productId`) based on event count, along with their counts. Option D correctly describes this output: the `productId` and its count for the 10 product IDs with the highest event counts.

Answer

All productId values sorted alphabetically

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ProductId values with count=0

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The top 10 productId values based on event count

Question 7

A visualization is showing unexpected spikes in a timechart. The data is aggregated by hour, but the spikes align with time zone changes. What is the likely cause?

Accepted Answer

The data contains events with different time zones. The unexpected spikes in the timechart are caused by events being timestamped with different time zones. When Splunk aggregates data by hour using `_time`, it converts all timestamps to the search head's local time zone. Events originally logged in different time zones (e.g., UTC vs. EST) will be shifted into different hour buckets, creating artificial spikes at the boundaries of time zone changes (e.g., daylight saving time transitions). This is a classic time zone misalignment issue in time-series analysis.

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The timechart uses a gap threshold

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The search uses _time instead of _indextime

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The bin span is too small

Question 8

Refer to the exhibit. Which visualization is most appropriate for this data?

Accepted Answer

Pie chart. A pie chart is the most appropriate visualization for this data because it shows a breakdown of a whole into its constituent parts, specifically the percentage of total events by source type. Pie charts are ideal for displaying proportional or categorical data where the sum of all slices equals 100%, making it easy to compare the relative size of each category at a glance.

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Single value

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Scatter chart

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Line chart

Question 9

A user wants to create a field that contains the length of the 'message' field. Which command should they use?

Accepted Answer

eval msg_length=len(message). The `eval` command in Splunk is used to create or modify fields, and the `len()` function returns the character length of a string field. The syntax `eval msg_length=len(message)` creates a new field named `msg_length` containing the length of the `message` field.

Answer

rex field=message "(?<msg_length>.*)"

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eval msg_length=sizeof(message)

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len(message)

Question 10

A security analyst creates a dashboard to monitor failed login attempts over the past 24 hours. Which visualization type is most appropriate for showing the trend of failed logins over time?

Accepted Answer

Line chart. A line chart is the most appropriate visualization for showing the trend of failed login attempts over time because it plots continuous data points along a time axis, allowing the analyst to easily identify patterns, spikes, or declines in the event count. In Splunk, a timechart command (e.g., `timechart count by action`) generates data that is best rendered as a line chart to display the temporal progression of failed logins over the past 24 hours.

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Single value

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Pie chart

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Scatter plot