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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Exploring Splunk's Search Processing Language | 15% | - Using transactions - Using workflow actions - Using advanced search commands - Using search macros - Using tags and event types |
| Topic 2: Exploring eval Command Functions | 4% | - Using conversion functions - Using comparison and conditional functions - Using makeresults command - Using statistical functions - Using informational functions - Using text functions |
| Topic 3: Exploring Search Optimization | 10% | - Using report acceleration - Using tsidx files - Using summary indexing - Using search optimization techniques |
| Topic 4: Exploring Data Models | 10% | - Understanding data models - Creating data models - Using pivot - Using data model objects |
| Topic 5: Exploring Alerts | 4% | - Using alert manager - Logging and indexing searchable alert events - Understanding alert actions - Referencing alert actions |
| Topic 6: Exploring Statistical Commands | 4% | - Using fieldsummary - Using eventstats - Using streamstats - Performing statistical analysis with stats function - Using appendpipe - Using count and list functions |
| Topic 7: Exploring Lookups | 4% | - Using external lookups - Using geospatial lookups - Using KV Store lookups - Understanding best practices for lookups - Applying advanced lookup options - Including and excluding events based on lookup values |
| Topic 8: Exploring Dashboards and Forms | 15% | - Using dynamic form inputs - Creating dashboards using Simple XML - Using drilldowns - Using event handlers - Using tokens |
| Topic 9: Exploring Field Extractions | 10% | - Using the Field Extractor - Creating custom fields - Using field aliases - Using calculated fields |
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NEW QUESTION # 45
Repeating JSON data structures within one event will be extracted as what type of fields?
Answer: A
Explanation:
When Splunk encounters repeating JSON data structures in an event, they are extracted as multivalue fields. These allow multiple values to be stored under a single field, which is common with arrays in JSON data.
NEW QUESTION # 46
Why use the tstats command?
Answer: A
Explanation:
The tstats command in Splunk is used to generate statistics on indexed fields, particularly from data models that have been accelerated (Option B). This command is highly efficient for summarizing large volumes of data because it operates on indexed-time summarizations rather than raw data, enabling faster search performance and reduced processing time. The tstats command is especially useful in scenarios where quick aggregation and analysis of indexed data are required, making it a powerful tool for exploring and reporting on data model information. While tstats can be seen as an alternative to some uses of the summary command (Option A), its primary utility is in its ability to leverage data model accelerations and indexed field statistics, rather than creating or referring to summary indexes. It does not specifically generate statistics on search-time fields (Option D) or create an accelerated data model (Option C), but rather it queries against existing accelerated data models.
NEW QUESTION # 47
Why is the transaction command slow in large splunk deployments?
Answer: C
Explanation:
The transaction command can be slow in large Splunk deployments because it requires all event data relevant to the transaction to be returned to the search head (Option C). This process can be resource-intensive, especially for transactions that span a large volume of data or time, as it involves aggregating and sorting events across potentially many indexers before the transaction logic can be applied.
NEW QUESTION # 48
What is the value of base lispy in the Search Job Inspector for the search index=sales clientip=170.
192.178.10?
Answer: C
Explanation:
The base lispy expression represents how Splunk parses and simplifies a search command. In this case, the lispy format shows how Splunk is breaking down the search terms to effectively perform the search.
NEW QUESTION # 49
Which of the following is accurate about cascading inputs?
Answer: C
Explanation:
Cascading inputs allow one input's selection to determine the options available in subsequent inputs. An event handler can reset the cascading sequence based on user interactions, ensuring the following inputs reflect appropriate options based on prior selections.
NEW QUESTION # 50
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