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Section 1
1.1 Use the chart command
Preview
Preview: 1.0 Using Transforming Commands for Visualizations (5%)
Preview includes- 4 of 5 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- 1.1 Use the chart command
- Key syntax and documented behavior
- Command or knowledge-object decision rules
- Worked exam-style examples
Overview
Use `chart` when the result should be a transformed table organized by categorical fields rather than by time.
Core Concepts
- `chart` is a transforming command: after it runs, the result is a statistical table rather than the original event stream.
- `OVER <field>` supplies the row split (commonly the x-axis category) and `BY <field>` supplies the column/series split.
- Use `chart` when the horizontal dimension is a category such as service, team, host, or product—not `_time`.
Exam Tips
- Find the x-axis clue first. Category → think `chart`; time → think `timechart`.
- If the stem says 'top N series', look for `limit`, not `head`.
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Section 2
1.2 Use the timechart command
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Section 3
2.1 The eval command
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Section 4
2.2 Use the search and where commands to filter results
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Section 5
2.3 The fillnull command
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Section 6
3.1 Identify transactions
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Section 7
3.2 Group events using fields
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Section 8
3.3 Group events using fields and time
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Section 9
3.4 Search with transactions
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Section 10
3.5 Report on transactions
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Section 11
3.6 Determine when to use transactions vs. stats
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Section 14
5.1 Describe, create, and use field aliases
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Section 15
5.2 Describe, create, and use calculated fields
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Section 16
6.1 Create and use tags
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Section 17
6.2 Describe event types and their uses
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Section 18
6.3 Create an event type
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Section 19
7.1 Describe macros
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Section 20
7.2 Create and use a basic macro
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Section 21
7.3 Define arguments and variables for a macro
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Section 22
7.4 Add and use arguments with a macro
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Section 23
8.1 Describe the function of GET, POST, and Search workflow actions
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Section 24
8.2 Create a GET workflow action
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Section 25
8.3 Create a POST workflow action
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Section 26
8.4 Create a Search workflow action
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Section 27
9.1 Describe the relationship between data models and pivot
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Section 28
9.2 Identify data model attributes
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Section 29
9.3 Create a data model
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Section 30
10.1 Describe the Splunk CIM
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Section 31
10.2 List the knowledge objects included with the Splunk CIM Add-On
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Section 32
10.3 Use the CIM Add-On to normalize data
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