Splunk SPLK-2002 Exam Dumps - Actual Questions Answers

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk’s official planning guidance explains that the deployment cycle begins with defining the system architecture, capacity planning, platform design, and topologies. Splunk states that before any component is installed, administrators must complete “infrastructure planning and buildout,” which includes determining indexer capacity, search head roles, clustering strategy, storage layout, and performance requirements. This foundational step ensures that all Splunk components have the proper hardware, network design, and scaling expectations.

After the environment is built, Splunk documentation states that the next stage is “deployment and data onboarding,” which includes configuring indexers, forwarders, parsing rules, event processing pipelines, data source validation, and enrichment steps such as field extractions, tagging, event types, and CIM alignment. Splunk describes this as the phase where you bring in data and confirm correctness, completeness, and normalization.

Only after the system is stable and populated with data does Splunk recommend “user planning and rollout”, which includes developing dashboards, roles, knowledge objects, search best practices, and enabling user access. Splunk emphasizes that user onboarding should occur last, once infrastructure and data pipelines are fully validated.

KV Store is a feature of Splunk Enterprise that allows apps to store and retrieve data within the context of an app1.

KV Store resides on search heads and replicates data across the members of a search head cluster1.

KV Store resiliency refers to the ability of KV Store to maintain data availability and consistency in the event of failures or disruptions2.

One of the factors that affects KV Store resiliency is the network latency between search heads, which can impact the speed and reliability of data replication2.

Decreasing latency between search heads can improve KV Store resiliency by reducing the chances of data loss, inconsistency, or corruption2.

The other options are not directly related to KV Store resiliency. Faster storage, indexer CPU and memory, and Operations Log size may affect other aspects of Splunk performance, but not KV Store345.

The Splunk Capacity Planning Manual defines the total required storage for indexes as a function of daily ingest rate, compression factor, retention period, and an additional padding buffer for index management and growth.

The formula is:

Storage = (Daily Data * Compression Factor * Retention Days) + Padding

Given the values:

Daily rate = 20 GB

Compression factor = 0.5 (50% reduction)

Retention period = 30 days

Padding = 100 GB

Plugging these into the formula gives:

20 * 0.5 * 30 + 100 = 400 GB

This result represents the estimated storage needed to retain 30 days of compressed indexed data with an additional buffer to accommodate growth and Splunk’s bucket management overhead.

Compression factor values typically range between 0.5 and 0.7 for most environments, depending on data type. Using compression in calculations is critical, as indexed data consumes less space than raw input after Splunk’s tokenization and compression processes.

Other options either misapply the compression ratio or the order of operations, producing incorrect totals.

References (Splunk Enterprise Documentation):

• Capacity Planning for Indexes – Storage Sizing and Compression Guidelines

• Managing Index Storage and Retention Policies

• Splunk Enterprise Admin Manual – Understanding Index Bucket Sizes

• Indexing Performance and Storage Optimization Guide