Splunk Enterprise Certified Architect - SPLK-2002 Exam Dumps

Explanation:

📖 The Formula
The correct formula for calculating Splunk index storage is:

(Daily Ingestion Rate × Compression Factor × Retention Period) + Padding

Applying the given values:

20 GB/day × 0.5 × 30 days + 100 GB
= 10 GB/day × 30 days + 100 GB
= 300 GB + 100 GB
= 400 GB ✅

📐 Understanding Each Parameter

Daily Rate: 20 GB/day
Raw uncompressed data ingested per day

Compression Factor: 0.5
Splunk typically compresses data to ~50% of original size

Retention Period: 30 days
How long data is kept in the index

Padding: 100 GB
Extra buffer for metadata, bloom filters, TSIDX files, and overhead

💡 Key Concept — Compression Factor

The compression factor represents how much Splunk reduces the raw data size on disk:

A factor of 0.5 means Splunk stores data at 50% of its original size
This is applied before multiplying by the retention period, since every day's ingested data gets compressed

Typical Splunk compression ratios range from 0.4 to 0.6 depending on data type.

❌ Why the Other Options Are Wrong

❌ A — (20 * 30 + 100) * 0.5 = 350 GB

(600 + 100) * 0.5 = 350 GB

Wrong because the compression factor is applied after adding padding.
Padding (metadata/overhead) should not be compressed — it is a fixed buffer added at the end.
Applying compression to padding underestimates the total storage needed.

❌ B — 20 / 0.5 * 30 + 100 = 1300 GB

40 * 30 + 100 = 1300 GB

Wrong because it divides by the compression factor instead of multiplying.
Dividing would imply data gets larger after compression, which is the opposite of reality.
This formula would apply if you were converting compressed size back to raw size, not calculating storage needed.

❌ D — 20 * 30 + 100 = 700 GB

600 + 100 = 700 GB

Wrong because it completely ignores the compression factor.
This calculates raw uncompressed storage, which would significantly overestimate actual disk usage.
Splunk always compresses indexed data, so this factor must be included.

📊 Side-by-Side Comparison

Option A — 350 GB → Compression applied incorrectly to padding
Option B — 1300 GB → Divides instead of multiplies
Option C — 400 GB → Correct calculation
Option D — 700 GB → Ignores compression

📚 Reference
Splunk Docs: Calculate your storage requirements
Splunk Docs: Index storage and data retention

Explanation:

In Splunk Enterprise, a license peer (such as an indexer) must regularly communicate with the license manager to validate licensing status.

If the peer cannot contact the license manager for 72 hours or longer:

The peer enters a license violation state
Search functionality is blocked
Indexing can continue, but users cannot perform searches on that peer’s data until communication is restored

This mechanism prevents long-term disconnected operation without license validation.

Why the Other Options Are Incorrect

“The license peer is placed in violation, and a warning is generated.”
Incomplete. The major consequence is that search gets blocked, not just a warning.

“A license warning is generated, and there is no impact to the license peer.”
Incorrect because there is operational impact after 72 hours.

“What happens depends on license type.”
Incorrect for this scenario. The 72-hour communication requirement applies generally to managed license peers.

Key Exam Point

For the SPLK-2002 exam, remember:

If a license peer cannot contact the license manager for 72 hours, it enters violation status and search is blocked.

This is different from daily indexing-volume violations, which typically require multiple violations before search restrictions occur.

Reference

Official documentation:
Splunk Enterprise Documentation – Configure License Peers and Managers

Explanation:

In a multi-site Splunk indexer cluster:

The Cluster Manager (also called the master node) is located at Site 1.

Site 2 loses connectivity to Site 1 due to a DNS failure (but the cluster network between sites is down, not necessarily all network functions).

Key behavior:

The cluster does not rely on the Cluster Manager for runtime search execution on existing data. Once buckets are assigned and peers know their primaries/searchable copies, search heads can query indexers directly for data that resides locally at Site 2, even if the Cluster Manager is unreachable.

What happens specifically at Site 2:

Indexer peers at Site 2 continue serving searches for data that has replicas in Site 2.

Search heads (configured with site=site2 or no site affinity) will still be able to search local buckets on Site 2 indexers.

Cluster Manager functions that are temporarily impaired because Site 2 cannot reach Site 1:

Bucket fix-up jobs
Generation transitions
Peer handshake/reconfiguration
Rerun of failed replication tasks
New bucket assignments across sites

However, the cluster does not stop responding to searches — that would happen only if all indexers failed, not just inter-site connectivity.

Why the other options are incorrect

A. The search heads search data on Site 1 and Site 2.
Incorrect – Site 2 cannot reach Site 1, so it cannot search data only on Site 1 without connectivity. It can only search its local replicas. Across-site search is impossible until link is restored.

C. The cluster stops responding to all searches.
Incorrect – This is far too extreme. Only cross-site search is impacted; intra-site searches at Site 2 still work.

D. The search heads start locally indexing the data.
Incorrect – Search heads do not index data unless specifically configured as heavy forwarders or indexers. They query indexers; they don’t “start indexing” during a network partition.

Reference

Splunk Docs – Multi-site indexer cluster

Site isolation: “If a site becomes disconnected from the Cluster Manager, indexers at that site continue to serve searches for existing buckets. Cluster management operations pause, but searching remains available within the isolated site.”

Splunk Enterprise Certified Architect official study guide – Disaster tolerance: “Loss of inter-site communication does not cause complete search failure; each site continues to serve local search traffic.”

Explanation:
The Splunk Universal Forwarder (UF) is a dedicated, lightweight version of Splunk Enterprise designed specifically for forwarding data. Unlike heavy forwarders or other Splunk components, the Universal Forwarder has its own license. This is because:
The UF is a separate binary distribution from Splunk Enterprise.
It is optimized for minimal resource usage and secure data forwarding.
Splunk provides the UF under a distinct license agreement, independent of the Splunk Enterprise license.
This separation ensures organizations can deploy thousands of forwarders without consuming Splunk Enterprise license capacity, while still complying with Splunk’s licensing terms.

Why the other options are incorrect

A. Splunk Cloud Forwarder
There is no separate “Splunk Cloud Forwarder” product. Splunk Cloud uses forwarders (usually Universal Forwarders) to send data, but they do not have their own license.

B. Splunk Heavy Forwarder
A heavy forwarder is simply a full Splunk Enterprise instance configured to forward data. It uses the standard Splunk Enterprise license, not a separate forwarder license.

D. Splunk Forwarder Management
This is a feature within Splunk Enterprise for managing forwarders. It is not a standalone offering and does not have its own license.

Key Exam Point
Only the Universal Forwarder has its own license. Heavy forwarders, forwarder management, and cloud forwarders all rely on Splunk Enterprise licensing.

References
Splunk Docs: About Splunk Universal Forwarder.
Splunk Licensing Guide: Types of Splunk licenses .

Explanation:

When a high-volume source and a low-volume source share the same index, two significant problems arise:

✅ B — Search Speed on Low Volume Data Will Be Slower Than Necessary

When both sources share an index, all searches against the low-volume data must scan through the buckets of the high-volume data as well, even if the high-volume data is irrelevant to the query.

Splunk searches at the bucket level, not the event level.
More buckets = more data to scan = slower searches.

If the low-volume data had its own dedicated index, searches would only scan a fraction of the buckets, resulting in significantly faster query times.

This is a core reason why separating sources into dedicated indexes is a Splunk architecture best practice.

✅ C — Low Volume Data May Move Out of the Index Based on Volume Rather Than Age

Splunk manages index retention using two limits:

maxTotalDataSizeMB — volume-based retention limit
frozenTimePeriodInSecs — age-based retention limit

Whichever limit is hit first triggers bucket freezing/deletion.

When a high-volume source shares an index with a low-volume source:

The volume cap (maxTotalDataSizeMB) is likely to be hit much sooner due to the high-volume data.

This causes older low-volume data to be frozen/deleted prematurely, even if it hasn't reached its intended retention age.

The low-volume data effectively gets "crowded out" by the high-volume data.

❌ Why Options A & D Are Wrong

A — Low volume data improves compression of high volume data
Splunk compresses data within buckets independently; mixing sources does not improve compression ratios. Compression is based on data similarity within a bucket, not cross-source interaction.

D — High volume data is optimized by the presence of low volume data
There is no such optimization in Splunk. High-volume data performance is not enhanced by co-existing with low-volume data — if anything, mixed sources add complexity without benefit.

💡 Best Practice Recommendation

Always separate sources with different volume profiles or retention requirements into dedicated indexes. This ensures:

Faster, more targeted searches
Accurate, predictable data retention
Easier access control with index-level permissions

📚 Reference
Splunk Docs: Create custom indexes
Splunk Docs: How index retention works

Explanation:

Starting with Splunk Enterprise, the _configtracker index is used to record and monitor configuration file changes, especially .conf file modifications across the Splunk environment.

This feature helps administrators:

Track configuration changes
Troubleshoot unexpected behavior
Audit administrative actions
Identify when and where configuration updates occurred

Examples of tracked files include:

inputs.conf
props.conf
transforms.conf
indexes.conf
server.conf

Why the Other Options Are Incorrect

_introspection
Stores operational and performance metrics about Splunk processes and resource usage (CPU, memory, disk, etc.), not config file changes.

_internal
Contains Splunk internal logs such as splunkd.log, scheduler activity, and system messages, but not dedicated configuration change tracking introduced in Splunk 9.0.

_audit
Tracks user actions and authentication events, such as searches run, logins, and role changes—not .conf file modifications.

Key Exam Point

For the SPLK-2002 exam, remember:

Splunk 9.x introduced _configtracker specifically for configuration file change tracking and auditing.

Reference
Official documentation:
Splunk Enterprise Documentation – Configuration Change Tracking

Explanation:
A search head cluster (SHC) is designed to provide high availability and scalability for the search head tier. Its primary functions are to ensure that if one search head fails, others can seamlessly take over, and to distribute the load of user searches. A core mechanism for achieving this is the automatic and continuous synchronization of knowledge objects (like saved searches, dashboards, and lookups) across all members of the cluster, ensuring a consistent user experience.

Correct Option:

C. Knowledge Object replication:
This is a fundamental and valid use case for a search head cluster. The SHC's internal replication mechanism automatically copies knowledge objects from one member to all others. This ensures that any saved search, dashboard, or lookup created by a user on one member is immediately available on every other member, maintaining configuration consistency and enabling true high availability.

Incorrect Options:

A. Provide redundancy in the event a search peer fails:
Redundancy for search peers (indexers) is provided by the indexer cluster, not the search head cluster. An indexer cluster uses the replication and search factors to ensure data remains available if a peer fails. A search head cluster provides redundancy for the search heads themselves.

B. Search affinity:
Search affinity is a feature of a multi-site indexer cluster. It directs search requests to indexer peers in the same site as the search head to minimize WAN traffic. This is not a function of a search head cluster.

D. Increased Search Factor (SF):
The Search Factor is a configuration parameter of an indexer cluster that determines how many searchable copies of the data are maintained. It is set on the cluster manager and has no relation to the search head cluster's functionality.

Reference:
Splunk Enterprise Admin Manual: "About search head clustering". The documentation lists the benefits of an SHC, which include the replication of knowledge objects to all cluster members and providing high availability for the search head function. It explicitly distinguishes these functions from those of an indexer cluster.

Explanation:
During indexing, Splunk performs line breaking, timestamp extraction, event building, and character set conversion. Most props.conf settings directly affect these CPU- and memory-intensive steps. The CHARSET setting only declares the character encoding of incoming data (e.g., UTF-8, UTF-16). It does not trigger re-encoding or complex parsing; it is simply read once per sourcetype and has negligible impact on indexing pipeline performance.

Correct Option:

C. CHARSET
CHARSET merely informs the indexer how the source data is encoded.

It is evaluated once per sourcetype at startup and does not participate in per-event processing.

No additional CPU cycles or memory are spent on character conversion unless the data is actually malformed, making it the least performance-impacting setting among the options.

Incorrect Option:

A. SHOULD_LINEMERGE
When set to true (default), Splunk performs expensive line-merging logic and regex evaluation (BREAK_ONLY_BEFORE, MUST_BREAK_AFTER, etc.), significantly increasing indexing CPU and memory usage.

B. TRUNCATE
Changing TRUNCATE from the default 10,000 bytes to a larger value forces the indexer to allocate and copy much larger event buffers into memory and disk, directly affecting indexing throughput and I/O.

D. TIME_PREFIX
TIME_PREFIX is a regex that tells Splunk where to start looking for the timestamp. An inefficient or missing TIME_PREFIX forces the indexer to scan more of each event (or the entire event), increasing CPU usage during timestamp extraction.

Reference:
Splunk Documentation → props.conf.spec → Performance impact notes

Explanation:
In a Splunk indexer cluster, data is stored in directories called "buckets." Each bucket is a self-contained unit that contains both the compressed raw data (the original event data in a journal file) and the index files (tsidx files) that allow for fast searching. The cluster's replication factor ensures a specified number of complete bucket copies exist across the peer nodes. The "searchable" and "replicated" states refer to the bucket's role in the cluster, not its internal composition.

Correct Options:

C. Inside a searchable bucket, there is tsidx and rawdata:
A searchable bucket is a complete copy of the data that is available for searching. It must contain both the tsidx files (the index) to find events quickly and the rawdata (the compressed journal file) to retrieve the full event text.

D. Inside a replicated bucket, there is both tsidx and rawdata:
The term "replicated bucket" refers to any bucket that has been copied to another peer node as part of the replication process. A replicated bucket is a full, bit-for-bit copy of the original bucket, meaning it also contains both the tsidx files and the rawdata journal.

Incorrect Options:

A. Inside a replicated bucket, there is only rawdata:
This is incorrect. A replicated bucket is not a partial copy. For a bucket to be usable on any peer, it must be a complete set of both the index files and the raw data.

B. Inside a searchable bucket, there is only tsidx:
This is incorrect. If a bucket only contained tsidx files, Splunk could determine that an event exists and its location but could not retrieve the actual event data. A searchable bucket must be fully functional, requiring both components.

Reference:
Splunk Enterprise Admin Manual: "How the indexer stores indexes". This documentation explains the structure of a bucket, which always consists of the rawdata file (compressed journal) and the index files (tsidx). The clustering functionality replicates these complete bucket directories between peers.

Explanation:
The Splunk KV store is a MongoDB‑based storage system embedded within Splunk Enterprise. It is used by apps and knowledge objects to persist structured data such as lookup tables, app state, and configuration metadata. At times, administrators may need to reset or clear the KV store, usually when corruption occurs, when troubleshooting replication issues, or when preparing a clean environment. The official and only supported command to clear the KV store is:

This command removes all KV store data from the Splunk instance. It is destructive and irreversible, meaning all KV store collections and documents are deleted. After running this command, Splunk reinitializes the KV store upon restart, creating a fresh, empty database. Because of its impact, Splunk recommends using it only when necessary and after backups if data is important.

The reason this command exists separately from other splunk clean options (like splunk clean eventdata or splunk clean all) is because KV store data is distinct from indexed data. Indexed data resides in buckets on disk, while KV store data resides in MongoDB collections. Clearing one does not affect the other. Thus, Splunk provides a dedicated command for KV store management.

Why the Other Options Are Incorrect

B. splunk clear kvstore
This command does not exist in Splunk’s CLI. Splunk’s syntax uses the verb clean, not clear. The distractor is designed to test whether you know the exact command wording. Attempting to run splunk clear kvstore will result in an error because Splunk does not recognize it.

C. splunk delete kvstore
Similarly, there is no delete kvstore command. Splunk does not use “delete” as a CLI verb for cleaning subsystems. The only valid verbs are start, stop, restart, status, and clean. “Delete” is a plausible but incorrect distractor.

D. splunk reinitialize kvstore
Splunk does not provide a reinitialize kvstore command. Reinitialization happens automatically after running splunk clean kvstore and restarting Splunk. The KV store service starts fresh, but there is no explicit CLI command called “reinitialize.” This distractor tests whether you understand Splunk’s actual CLI syntax versus imagined commands.

References
Splunk Docs: Clean the KV store
Splunk Docs: — About KV store
Splunk Admin Guide: CLI commands