Free Splunk SPLK-1002 Practice Exam with Questions & Answers | Set: 4

The correct answer is D. | eval notNULL = “” fillnull value=0 notNULL

Option A is incorrect because it is missing a comma between the “0” and the notNULL in the if function. The correct syntax for the if function is if (condition, true_value, false_value).

Option B is incorrect because it is missing the false_value argument in the if function. The correct syntax for the if function is if (condition, true_value, false_value).

Option C is incorrect because it uses the nullfill command, which only replaces null values, not empty strings. The nullfill command is equivalent to fillnull value=null.

Option D is correct because it uses the eval command to assign an empty string to the notNULL field, and then uses the fillnull command to replace the empty string with a zero. The fillnull command can replace any value with a specified replacement, not just null values.

Field aliases are applied based on host, source, or sourcetype.

Extract: “Create field aliases by specifying the host, source, or sourcetype that the alias applies to.”

Thus, the correct answer is Sourcetypes.

By default, acceleration is determined automatically based on the data source in the Splunk Common Information Model (CIM) add-on. The Splunk CIM Add-on is an app that provides common data models for various domains, such as network traffic, web activity, authentication, etc. The CIM Add-on allows you to normalize and enrich your data using predefined fields and tags. The CIM Add-on also allows you to accelerate your data models for faster searches and reports. Acceleration is a feature that pre-computes summary data for your data models and stores them in tsidx files. Acceleration can improve the performance and efficiency of your searches and reports that use data models.

By default, acceleration is determined automatically based on the data source in the CIM Add-on. This means that Splunk will decide whether to enable or disable acceleration for each data model based on some factors, such as data volume, data type, data model complexity, etc. However, you can also manually enable or disable acceleration for each data model by using the Settings menu or by editing the datamodels.conf file.

The correct answer is C. When events contain unstructured data.

The regular expression method works best with unstructured event data, such as log files or text messages, where the fields are not separated by a common delimiter, such as a comma or space1. You select a sample event and highlight one or more fields to extract from that event, and the field extractor generates a regular expression that matches similar events in your dataset and extracts the fields from them1. The regular expression method provides several tools for testing and refining the accuracy of the regular expression. It also allows you to manually edit the regular expression1.

The delimiters method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space1. You select a sample event, identify the delimiter, and then rename the fields that the field extractor finds1. This method is simpler and faster than the regular expression method, but it may not work well with complex or irregular data formats1.

One of the valid options to speed up reports is to edit acceleration, which means that you can enable summary indexing or data model acceleration for your reports to improve their performance2. Summary indexing allows you to create reports that run over large amounts of data by storing the results of scheduled searches in a summary index and using that index for faster reporting2. Data model acceleration allows you to create reports that use data models by creating and storing summaries of the data model datasets and using them for faster reporting2. Therefore, option C is correct, while options A, B and D are incorrect because they are not options to speed up reports.

When using the Field Extractor (FX) tool in Splunk and the tool fails to extract a value from all appropriate events, there are specific steps you can take to improve the extraction process. These steps involve interacting with the FX tool and possibly adjusting the extraction method:

A. Select an additional sample event with the Field Extractor (FX) and highlight the missing value in the event. This approach allows Splunk to understand the pattern better by providing more examples. By highlighting the value in another event where it wasn't extracted, you help the FX tool to learn the variability in the data format or structure, improving the accuracy of the field extraction.

D. Edit the regular expression manually. Sometimes the FX tool might not generate the most accurate regular expression for the field extraction, especially when dealing with complex log formats or subtle nuances in the data. In such cases, manually editing the regular expression can significantly improve the extraction process. This involves understanding regular expression syntax and how Splunk extracts fields, allowing for a more tailored approach to field extraction that accounts for variations in the data that the automatic process might miss.

Options B and C are not typically related to improving field extraction within the Field Extractor tool. Re-ingesting data (B) does not directly impact the extraction process, and changing to a delimited extraction method (C) is not always applicable, as it depends on the specific data format and might not resolve the issue of missing values across events.

The correct answer is B. Delimiters. This is because the delimiters method is designed for structured event data, such as data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space. You can select a sample event, identify the delimiter, and then rename the fields that the field extractor finds. You can learn more about the delimiters method from the Splunk documentation1. The other options are incorrect because they are not suitable for comma-separated data. The regular expression method works best with unstructured event data, where you select and highlight one or more fields to extract from a sample event, and the field extractor generates a regular expression that matches similar events and extracts the fields from them. The eval expression is a command that lets you calculate new fields or modify existing fields using arithmetic, string, and logical operations. The table extraction is a feature that lets you extract tabular data from PDF files or web pages. You can learn more about these methods from the Splunk documentation23 .

The correct answer is A. When a log file has values that are separated by the same character, for example, commas.

The Field Extractor (FX) is a utility in Splunk Web that allows you to create new fields from your events by using either regular expressions or delimiters. The FX provides a graphical interface that guides you through the steps of defining and testing your field extractions1.

The FX supports two field extraction methods: regular expression and delimited. The regular expression method works best with unstructured event data, such as logs or messages, that do not have a consistent format or structure. You select a sample event and highlight one or more fields to extract from that event, and the FX generates a regular expression that matches similar events in your data set and extracts the fields from them1.

The delimited method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma, a tab, or a space. You select a sample event, identify the delimiter, and then rename the fields that the FX finds1.

Therefore, you would select the delimited field extraction method when you have a log file that has values that are separated by the same character, for example, commas. This method will allow you to easily extract the fields based on the delimiter without writing complex regular expressions.

The other options are not correct because they are not suitable for the delimited field extraction method. These options are:

B. When a log file contains empty lines or comments: This option does not indicate that the log file has a structured format or a common delimiter. The delimited method might not work well with this type of data, as it might miss some fields or include some unwanted values.

C. With structured files such as JSON or XML: This option does not require the delimited method, as Splunk can automatically extract fields from JSON or XML files by using indexed extractions or search-time extractions2. The delimited method might not work well with this type of data, as it might not recognize the nested structure or the special characters.

D. When the file has a header that might provide information about its structure or format: This option does not indicate that the file has a common delimiter between the fields. The delimited method might not work well with this type of data, as it might not be able to identify the fields based on the header information.

Tags can be searched with tag= and Boolean operators.

Extract: “Use tag= to search for events with that tag. Use Boolean operators such as NOT to exclude tags.”

Therefore, tag=Marketing NOT (tag=San_Francisco) correctly filters Marketing-tagged employees excluding those tagged San_Francisco.