Splunk Enterprise Certified Architect SPLK-2002 Updated Exam

According to the Splunk blog1, the best method for sizing or scaling a search head cluster is to estimate the maximum concurrent number of searches and divide by the number of CPU cores per search head. This gives you an idea of how many search heads you need to handle the peak search load without overloading the CPU resources. The other options are false because:

Estimating the maximum daily ingest volume in gigabytes and dividing by the number of CPU cores per search head is not a good method for sizing or scaling a search head cluster, as it does not account for the complexity and frequency of the searches. The ingest volume is more relevant for sizing or scaling the indexers, not the search heads2.

Estimating the total number of searches per day and dividing by the number of CPU cores available on the search heads is not a good method for sizing or scaling a search head cluster, as it does not account for the concurrency and duration of the searches. The total number of searches per day is an average metric that does not reflect the peak search load or the search performance2.

Dividing the number of indexers by three to achieve the correct number of search heads is not a good method for sizing or scaling a search head cluster, as it does not account for the search load or the search head capacity. The number of indexers is not directly proportional to the number of search heads, as different types of data and searches may require different amounts of resources2.

According to the Splunk documentation1, metrics.log is a file that contains various metrics data for reviewing product behavior, such as pipeline, queue, thruput, and tcpout_connections. Metrics.log is stored in the _internal index by default2, which is a special index that contains internal logs and metrics for Splunk Enterprise. The other options are false because:

main is the default index for user data, not internal data3.

_telemetry is an index that contains data collected by the Splunk Telemetry feature, which sends anonymous usage and performance data to Splunk4.

_introspection is an index that contains data collected by the Splunk Monitoring Console, which monitors the health and performance of Splunk components.

The _internal index contains license-related events, such as the license usage, the license quota, the license pool, the license stack, and the license violations. These events are logged by the license manager in the license_usage.log file, which is part of the _internal index. The _audit index contains audit events, such as user actions, configuration changes, and search activity. These events are logged by the audit trail in the audit.log file, which is part of the _audit index. The _license index does not exist in Splunk, as the license-related events are stored in the _internal index. The _introspection index contains platform instrumentation data, such as the resource usage, the disk objects, the search activity, and the data ingestion. These data are logged by the introspection generator in various log files, such as resource_usage.log, disk_objects.log, search_activity.log, and data_ingestion.log, which are part of the _introspection index. For more information, see About Splunk Enterprise logging and [About the _internal index] in the Splunk documentation.

Data retention is optional in the data source assessment because it is not directly related to the ingestion process. Data retention is determined by the index configuration and the storage capacity of the Splunk platform. Data format, data location, and data volume are all essential information for planning how to collect, parse, and index the data source.