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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Exploring Statistical Commands | 4% | - Using eventstats - Performing statistical analysis with stats function - Using count and list functions - Using appendpipe - Using streamstats - Using fieldsummary |
| Topic 2: Exploring Search Optimization | 10% | - Using summary indexing - Using tsidx files - Using search optimization techniques - Using report acceleration |
| Topic 3: Exploring Data Models | 10% | - Using data model objects - Understanding data models - Creating data models - Using pivot |
| Topic 4: Exploring Lookups | 4% | - Understanding best practices for lookups - Using external lookups - Applying advanced lookup options - Including and excluding events based on lookup values - Using geospatial lookups - Using KV Store lookups |
| Topic 5: Exploring Dashboards and Forms | 15% | - Using drilldowns - Using dynamic form inputs - Using tokens - Using event handlers - Creating dashboards using Simple XML |
| Topic 6: Exploring eval Command Functions | 4% | - Using comparison and conditional functions - Using conversion functions - Using text functions - Using informational functions - Using statistical functions - Using makeresults command |
| Topic 7: Exploring Splunk's Search Processing Language | 15% | - Using transactions - Using workflow actions - Using tags and event types - Using search macros - Using advanced search commands |
| Topic 8: Exploring Field Extractions | 10% | - Using calculated fields - Using the Field Extractor - Creating custom fields - Using field aliases |
| Topic 9: Exploring Alerts | 4% | - Referencing alert actions - Using alert manager - Understanding alert actions - Logging and indexing searchable alert events |
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NEW QUESTION # 90
Which is a regex best practice?
Answer: A
Explanation:
One of the best practices in regex is to avoid backtracking, which can degrade performance by revisiting parts of the input multiple times. Optimizing regex patterns to prevent unnecessary backtracking improves efficiency, especially when dealing with large datasets.
NEW QUESTION # 91
What is the value of base lispy in the Search Job Inspector for the search index=sales clientip=170.
192.178.10?
Answer: D
Explanation:
In Splunk, the "base lispy" is an internal representation of the search query used by the Search Job Inspector.
It breaks down the search into its fundamental components for processing. For the search index=sales clientip=170.192.178.10, Splunk tokenizes the IP address into its individual octets and combines them with the index specification.
Therefore, the base lispy representation would be:
[ index::sales 192 AND 10 AND 178 AND 170 ]
This indicates that the search is constrained to the sales index and is looking for events containing all the specified IP address components.
NEW QUESTION # 92
A report named "Linux logins" populates a summary index with the search string sourcetype=linux_secure | sitop src_ip user. Which of the following correctly searches against the summary index for this data?
Answer: D
Explanation:
When searching a summary index, using search_name="Linux logins" ensures you retrieve data generated by that specific report. Option B correctly searches the summary index by referencing the report's name.
NEW QUESTION # 93
When enabled, what drilldown action is performed when a visualization is clicked in a dashboard?
Answer: B
Explanation:
Comprehensive and Detailed Step by Step Explanation:
When drilldown is enabled in a Splunk dashboard, clicking on a visualization triggers arefresh of the search results for the selected visualization. This allows users to interact with the data and refine the displayed results based on the clicked value.
Here's why this works:
* Drilldown Behavior: Drilldown actions are configured to dynamically update tokens or filters based on user interactions. When a user clicks on a chart, table, or other visualization, the underlying search query is updated to reflect the selected value.
* Contextual Updates: The refresh applies only to the selected visualization, ensuring that other panels in the dashboard remain unaffected unless explicitly configured otherwise.
Other options explained:
* Option A: Incorrect because visualizations are not automatically opened in a new window during drilldown.
* Option C: Incorrect because drilldown actions typically affect only the selected visualization, not all panels in the dashboard.
* Option D: Incorrect because a new search window is not opened unless explicitly configured in the drilldown settings.
Example:
<drilldown>
<set token="selected_value">$click.value$</set>
</drilldown>
In this example, clicking on a value updates theselected_valuetoken, which can be used to filter the visualization's search results.
References:
Splunk Documentation on Drilldowns:https://docs.splunk.com/Documentation/Splunk/latest/Viz
/DrilldownIntro
Splunk Documentation on Tokens:https://docs.splunk.com/Documentation/Splunk/latest/Viz
/UseTokenstoBuildDynamicInputs
NEW QUESTION # 94
When should summary indexing be used?
Answer: C
Explanation:
Comprehensive and Detailed Step by Step Explanation:Summary indexing should be used forreports that run on small datasets over long time ranges. It is particularly useful when you need to aggregate data over extended periods without querying raw events repeatedly.
Here's why this works:
* Efficiency: Summary indexing pre-aggregates data into summary indexes, reducing the amount of data that needs to be processed during runtime. This improves performance for reports that span long time ranges.
* Small Datasets: Summary indexing is most effective when working with smaller datasets because aggregating large volumes of data can become resource-intensive.
Other options explained:
* Option B: Incorrect because summary indexing is not a fallback for reports that fail to qualify for acceleration methods like report or data model acceleration.
* Option C: Incorrect because summary indexing is less beneficial for short time ranges, where querying raw data is often faster.
* Option D: Incorrect because Smart Mode is unrelated to summary indexing; it is a search optimization feature.
Example: Suppose you want to calculate daily sales totals over a year. Instead of querying raw sales data every time, you can use summary indexing to store daily totals and query the summary index instead.
References:
* Splunk Documentation on Summary Indexing:https://docs.splunk.com/Documentation/Splunk/latest
/Knowledge/Usesummaryindexing
* Splunk Documentation on Report Acceleration:https://docs.splunk.com/Documentation/Splunk/latest
/Knowledge/Acceleratedatamodels
NEW QUESTION # 95
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