Learning Splunk - Normalizing Data Using the Splunk Common Information Model (CIM)

The video tutorial explains how to normalize data using Splunk's Common Information Model (CIM). It highlights the default behavior of Splunk in extracting fields based on key-value pairs and the challenges posed by inconsistent logging across different firewalls. The tutorial introduces CIM as a solution to standardize field names, making data easily searchable and accessible. It also demonstrates how CIM can be applied to network traffic data, ensuring consistency and predictability in field names regardless of the vendor.

What is the default behavior of Splunk when extracting fields from log data?

• It ignores key-value pairs.

• It extracts fields based on key-value pairs.

• It uses predefined field names.

• It requires manual configuration for each log.

Why is consistency in field names important in Splunk?

• To reduce data storage requirements.

• To make field names predictable and data easily searchable.

• To allow for automatic data encryption.

• To ensure compatibility with all firewall vendors.

What does the Common Information Model (CIM) in Splunk provide?

• A way to encrypt log data.

• A tool for visualizing data trends.

• A framework for consistent field identification.

• A method to compress data.

Which of the following is an example of a standardized field name in CIM for network traffic?

• SourceIP

• IPSource

• IP_SRC

• SRC_IP

What values does Splunk expect for the 'action' field in network traffic data?

• Start, Stop, Pause

• Allow, Deny, Ignore

• Allowed, Blocked, Tear Down

• Open, Close, Reset