Splunk Enterprise Certified Architect - SPLK-2002 Exam Dumps

Explanation

This question tests the absolute minimum architectural requirements needed for a supported Splunk Search Head Cluster (SHC) deployment and how quorum is maintained using the Raft consensus algorithm.

Why A is correct
A functional Search Head Cluster requires two fundamental components:

1. Minimum of three (3) Search Heads
SHC relies on the Raft consensus algorithm, which requires a strict majority (quorum) to elect and maintain a captain.

• With 3 nodes, quorum = 2
• If 1 node fails, 2 nodes remain → quorum is still maintained (66%)
• This ensures the cluster continues operating and captain election remains stable

If only 2 nodes are used:
• Quorum would be 2
• Failure of one node leaves 1/2 → no majority → cluster instability or outage

2. A standalone SHC Deployer instance
The Deployer is responsible for distributing apps and configuration bundles to SHC members. It is a mandatory component, even though it is not part of the cluster itself.

Why B is incorrect
The SHC Deployer cannot run on a Search Head that is part of the cluster it manages. It must be a separate, independent instance to avoid circular dependency and configuration conflicts.

Why C is incorrect
A Deployment Server cannot replace an SHC Deployer. They serve completely different purposes:

• Deployment Server → manages forwarders and non-clustered instances
• SHC Deployer → manages app/config distribution for Search Head Clusters

These mechanisms are not interchangeable.

Why D is incorrect
While it is valid in some architectures to co-host the Deployer role on another management system (like a Deployment Server or Cluster Manager), this does not remove the requirement for at least three Search Heads. The SHC still requires a minimum of 3 nodes for quorum.

Key Exam Point
For a supported SHC deployment in Splunk:
• Minimum 3 Search Heads (for quorum and fault tolerance)
• 1 dedicated or co-hosted Deployer (outside the cluster)

Reference
Splunk Docs: Search Head Clustering system requirements
Splunk Enterprise Certified Architect: SHC architecture, quorum mechanics, and component roles

Explanation:

When managing a very large number of Splunk forwarders (e.g., 20,000), the Deployment Server can become a performance bottleneck because it must manage configuration distribution (apps and server classes) across all clients. Forwarders periodically “phone home” to check for updates, and excessive load can degrade deployment efficiency.

Why the Correct Answer is to limit server classes to 5,000 clients
Splunk best practices recommend keeping each server class to a maximum of around 5,000 clients. This ensures that:
• Configuration bundle distribution remains efficient
• Phone-home requests are evenly distributed over time
• The Deployment Server is not overwhelmed by simultaneous client connections
• Large environments scale predictably and remain manageable

By splitting 20,000 forwarders into multiple server classes, you reduce load and improve stability of deployment operations.

Why the Other Options Are Incorrect

Increase the phone-home interval
This may reduce how often forwarders contact the Deployment Server, but it does not solve the core scalability limitation of oversized server classes. It only delays load rather than properly distributing it.

Use workload management for client pools
Workload management applies to search workload distribution on search heads, not Deployment Server operations. It has no impact on forwarder management or server class scaling.

Reduce the polling interval for clients
Reducing the polling (phone-home) interval would actually increase the number of requests to the Deployment Server, making performance worse rather than improving scalability.

Key Exam Point
For large Splunk deployments, always design server classes carefully and keep them under ~5,000 clients each to maintain Deployment Server performance and avoid bottlenecks.

Reference
Splunk Docs — Deployment Server scaling guidelines:
“For large environments, limit server classes to 5,000 clients each to avoid performance degradation when managing forwarders.”

Explanation:
A deployment plan defines how a Splunk environment will be rolled out, who is responsible, and what activities must happen to ensure a smooth and successful implementation. One of the most essential components of this plan is a complete list of stakeholders, including both direct contributors and indirect influencers. Splunk stresses that deployment success strongly depends on aligning teams, gathering requirements from all stakeholders, and ensuring that everyone who relies on Splunk is identified early.

A stakeholder list is critical because Splunk deployments touch multiple technical and business domains. The platform depends on collaboration between system administrators, network engineers, security teams, compliance units, storage administrators, business analysts, SOC teams, and application owners. Without identifying these stakeholders upfront, the project lacks clarity on responsibilities, data ownership, approval flows, and operational governance. This often results in delays in firewall rule changes, incorrect hardware sizing, unclear data onboarding priorities, and misalignment of expectations between business users and Splunk administrators.

Splunk’s planning guidance specifically highlights the need to identify who owns the data, who manages the infrastructure, who consumes the results, and who will maintain the system. These groups form the core of the deployment governance model, which must be outlined before designing or implementing the architecture. Including a stakeholder list in the deployment plan ensures that requirement gathering is complete, communication flows are clearly defined, and decisions around architecture, data inputs, search workloads, and security controls are aligned with business and technical objectives.

In the Splunk Deployment Planning framework, the first stages—assessment and planning—require gathering detailed information from teams across the organization. This is only possible when all stakeholders are clearly documented. Splunk Validated Architectures and Splunk Deployment Planning documentation emphasize that stakeholder alignment is mandatory in the planning phase. Without this, deployment teams cannot confirm requirements such as data retention needs, search concurrency expectations, cluster sizing, network bandwidth availability, and compliance constraints.

Therefore, option D is the correct choice because a deployment plan must define stakeholders to ensure coordination, requirement clarity, and project accountability.

References:
Splunk Enterprise Deployment Planning Guide – Planning Activities (roles, responsibilities, stakeholders)
Architecting Splunk Enterprise Deployments – Requirement Gathering and Stakeholder Alignment
Splunk Validated Architectures – Pre-deployment Planning Considerations

❌ Why the Other Options Are Not Correct (Brief and Direct)

A. Business continuity and disaster recovery plans
Although important at the organizational level, BCP and DR are not core components of the Splunk deployment plan. They relate to maintaining availability during disruptions, not the initial deployment coordination. Splunk treats DR/backup planning as post-deployment operational considerations, not part of the deployment plan.

B. Current logging details and data source inventory
A logging inventory is necessary for data onboarding and helps with capacity planning, but it is not part of the deployment plan itself. Splunk separates deployment planning (people/process coordination) from input/data planning (source types, volumes, parsing needs).

C. Current and future topology diagrams of the IT environment
Topology diagrams belong to the architecture design document, not the deployment plan. Splunk treats architecture design and deployment planning as distinct stages. Topology diagrams help describe indexer clusters, SHCs, forwarders, and network zones, but they are not required to be part of the deployment plan.

Explanation:

When deploying Splunk in a virtualized environment such as VMware, Hyper-V, or other hypervisors, performance is generally lower compared to running on physical (bare-metal) hardware. This is due to virtualization overhead, shared resource contention, and additional I/O latency introduced by the hypervisor layer.

Splunk’s official guidance indicates that virtualized environments typically experience a performance reduction of 20% to 45% compared to equivalent physical hardware. This impact must be considered during capacity planning to ensure sufficient CPU, memory, and disk I/O resources for indexing and search workloads.

Why B is correct
A performance degradation range of 20% to 45% aligns with Splunk’s documented expectations for virtualized deployments. This range accounts for overhead introduced by virtualization layers and helps architects properly size infrastructure to maintain stable performance.

Why the other options are incorrect
Up to 15%
This value is too low and does not reflect real-world overhead observed in virtualized Splunk environments. Splunk documentation indicates a higher impact range.

0%
This is unrealistic because virtualization always introduces some level of overhead due to abstraction from physical hardware.

0.5
This is not a valid performance metric in this context and does not correspond to Splunk’s documented sizing guidance.

Reference
Splunk Docs — Virtualization and Splunk
“Splunk deployments in virtualized environments can experience a performance reduction of 20–45% compared to physical hardware.”

Explanation:

The Splunk Monitoring Console (MC) gathers data from multiple sources to populate its dashboards, such as Indexing Performance, Search Performance, License Usage, and System Health. These sources allow it to provide a centralized view of the entire Splunk deployment.

A — REST API calls
The Monitoring Console uses Splunk’s REST management endpoints (under /services/...) to retrieve real-time and historical information from various Splunk components such as indexers, search heads, license managers, and cluster managers.

Examples include:
* /services/server/info
* /services/admin/licenseusage
* /services/cluster/master/peers

These REST calls provide structured data that the MC uses to build dashboards and system health views.

D — metrics.log
The metrics.log file, located at $SPLUNK_HOME/var/log/splunk/metrics.log, contains detailed performance metrics from each Splunk instance.

It includes information such as:
* CPU and memory usage
* Disk I/O statistics
* Indexing throughput
* Search concurrency
* Queue and pipeline performance metrics

The Monitoring Console collects and aggregates this data from multiple nodes to generate performance dashboards and health reports.

Why the other options are incorrect
B — Splunk btool
btool is a configuration inspection and debugging utility used manually via CLI. It is not a continuously polled data source and does not provide runtime metrics to the Monitoring Console.

C — splunk diag
The splunk diag command is used to generate diagnostic bundles (logs, configurations, and system state) for troubleshooting and support cases. It is not used as a live data source for Monitoring Console dashboards.

Reference
Splunk Docs – Monitoring Console data sources
“The Monitoring Console gathers data from two primary sources: internal logs (especially metrics.log) and REST API calls to Splunk instances.”

SPLK-2002 Blueprint – Monitoring and Troubleshooting
“MC dashboards rely on REST endpoints and aggregated metrics.log data from across the deployment.”

Explanation:

The correct way to increase logging verbosity for a single Splunk search is by using the noop command directly within the SPL query. This allows targeted debugging without affecting global system settings.

Why B is correct
The noop command is an internal Splunk utility used to influence search behavior without altering results. By appending:
| noop log_debug=*

to a search, Splunk increases logging verbosity for that specific search job only. This causes additional debug-level information to be written into the search.log of that job, which can then be viewed in the Search Job Inspector.

Key benefits of this approach:
* Applies only to the current search job
* Does not require system restart
* Does not modify global logging configuration
* Useful for precise, per-search troubleshooting

Why A is incorrect
Setting infocsv_log_level = DEBUG in limits.conf enables debug logging globally across the Splunk instance. While it can expose detailed logs, it affects all searches, not just a single one.

This approach can also significantly increase log volume and negatively impact system performance, making it unsuitable for targeted troubleshooting.

Why C is incorrect
The Search Job Inspector is a diagnostic tool used to view details of a completed or running search, including execution statistics and logs. However, it does not provide any mechanism to modify or increase logging levels dynamically for a search job.

It is purely observational and not a configuration or control interface.

Why D is incorrect
The Splunk Web path Settings → Server settings → Server logging changes logging levels at the system level. These changes apply globally to Splunk components such as splunkd.

This method cannot isolate logging changes to a single search and therefore is not suitable for per-search debugging.

Reference
Splunk Docs: Internal search commands and noop command reference
Splunk Enterprise Certified Architect: Search troubleshooting and Job Inspector usage

Explanation:

Splunk provides multiple supported methods to change internal logging levels, giving administrators flexibility to troubleshoot issues across different environments. All four options listed are valid ways to adjust logging verbosity in Splunk Enterprise.

Understanding Logging Levels
Splunk logging levels determine how much detail is written to logs. They are ordered from least to most verbose as follows:

FATAL → ERROR → WARN → INFO → DEBUG → TRACE

Higher verbosity levels such as DEBUG and TRACE generate significantly more log data and can impact system performance. They should only be used temporarily during troubleshooting and reverted back to INFO afterward.

A — Monitoring Console (MC)
The Monitoring Console provides a GUI-based method to manage logging levels across Splunk deployments.

Navigation path:
Monitoring Console → Settings → Logging

Capabilities:
Change log levels for specific components or channels
Apply changes without restarting Splunk
Centralized management in distributed environments

This method is especially useful for large deployments where multiple nodes must be monitored and adjusted from a single interface.

B — Splunk Command Line Interface (CLI)
The Splunk CLI allows direct control over logging levels from the command line.

Examples:
splunk list log-level
splunk set log-level SearchScheduler -level DEBUG
splunk set log-level TcpInputProc -level TRACE
splunk set log-level LicenseManager -level WARN
splunk set log-level SearchScheduler -level INFO

Key characteristics:
Changes take effect immediately without restart
Can target specific components precisely
Useful for remote troubleshooting via SSH
Typically temporary unless persisted in configuration files

C — Splunk Web
Splunk Web provides a GUI method for adjusting logging levels.

Navigation path:
Settings → Server Settings → Server Logging

Capabilities:
View all logging channels/components
Adjust logging levels using dropdown menus
Apply changes immediately without restart

This method is commonly used by administrators who prefer UI-based configuration over CLI.

D — Edit log-local.cfg
The log-local.cfg file provides a persistent, file-based method of configuring logging levels.

File location:
$SPLUNK_HOME/etc/log-local.cfg

Key characteristics:
Changes persist across restarts
Requires restart to take effect
Overrides default log.cfg settings
Useful for long-term or permanent logging configuration changes

Example:
[SearchScheduler]
rootCategory = DEBUG, splunkd

[TcpInputProc]
rootCategory = TRACE, splunkd

Method Comparison Summary
Monitoring Console: No restart, temporary changes
Splunk CLI: No restart, temporary changes
Splunk Web: No restart, temporary changes
log-local.cfg: Requires restart, persistent changes

Key Considerations
Always revert logging back to INFO after troubleshooting
High verbosity can significantly increase disk usage
In distributed environments, changes may need to be applied per node
log-local.cfg changes are not automatically propagated across clusters

Reference
Splunk Docs: Set logging levels using Splunk Web
Splunk Docs: splunk set log-level CLI command

Explanation:

In Splunk Enterprise, the repFactor setting in indexes.conf controls whether an index participates in indexer cluster replication. It determines how bucket replication behaves across clustered indexers.

When configured as:
repFactor = 0

the index is excluded from indexer cluster replication entirely.

What repFactor = 0 means
When an index is configured with repFactor = 0, Splunk treats it as a non-replicated index. This results in the following behavior:

* Buckets are not replicated to other indexers
* The index does not participate in cluster redundancy mechanisms
* Data remains only on the local indexing peer where it was ingested

This configuration is typically used for non-critical or special-purpose data such as:
* Transient or temporary data
* Summary indexes
* Local-only or non-redundant datasets

Why the other options are incorrect
available_sites = none
This is not a valid configuration setting in indexes.conf and has no effect on cluster replication behavior.

repFactor = auto
This is the default behavior in clustered environments, where replication is controlled by the cluster’s global replication factor settings. It does not disable replication.

site_mappings = default_mapping
This setting is related to multisite cluster site assignment and bucket placement, not to disabling replication or excluding an index from clustering.

Key Exam Point
For SPLK-2002, remember that:

repFactor = 0 means the index is excluded from replication and does not participate in indexer clustering redundancy.

Example
[summary]
repFactor = 0

Explanation:
When ingesting syslog data into Splunk, the recommended best practice is to never send syslog traffic directly to Splunk indexers or search heads, and never rely on Splunk software to act as the primary syslog receiver. Instead, syslog should write incoming messages to files on disk—using a dedicated syslog server such as rsyslog, syslog-ng, or syslog-mp—and a Splunk Universal Forwarder should monitor those files and forward the events to Splunk indexers. Therefore, the correct choice is D.

Splunk explicitly recommends decoupling the syslog receiving function from the Splunk indexing layer. Splunk processes are not optimized to act as high-volume network syslog listeners, especially on port 514, which often handles thousands to millions of events per second from routers, switches, and firewalls. A traditional syslog daemon is designed to perform efficient buffering, queue management, and log-rotation, whereas Splunk is designed for indexing and search—not raw network packet listening.

By having syslog write events to disk first, you achieve several architectural benefits:

Reliability and data durability: Syslog daemons can buffer and queue incoming events if downstream services fail. Splunk listeners cannot do this reliably at syslog scale.

Load management: You can consolidate syslog traffic from many devices into a single, well-tuned syslog server instead of overwhelming indexers.

Data integrity: Writing logs to disk provides a stable, auditable source of truth before ingestion.

Scalability: You can easily scale forwarders and syslog receivers without redesigning the Splunk indexing tier.

Security/permissions: The forwarder runs with minimal privileges and only needs read access to log files, avoiding the need for Splunk indexers to open privileged ports like 514.

Best-practice alignment: Splunk documentation and architect guides consistently state that syslog inputs must not be received directly by indexers.

After storage, the Universal Forwarder monitors the log files using monitor or uf_tail inputs and forwards structured events to the indexers. This ensures proper parsing, timestamping, and line-breaking based on Splunk’s data onboarding best practices.

References:
Splunk Best Practices for Syslog Data – Do not send syslog directly to indexers; always use a dedicated syslog server writing to disk.
Splunk Data Administration Guide – Inputs Best Practices (Use UF to monitor syslog files, avoid listening directly on 514).
Architecting Splunk Deployments – Data Ingestion Layer Recommendations (Syslog should land on disk before Splunk).
Splunk Forwarder Manual – Monitoring Files and Directories (HF/UF used for file-based syslog ingestion).
These official guidance points consistently reinforce that writing syslog to disk first is not just a general suggestion—it is Splunk’s validated architecture requirement for large-scale, stable deployments.
For these reasons, D is the correct answer.

❌ Why the Other Options Are Incorrect (Brief)

A. Configure syslog to send the data to multiple Splunk indexers.
Splunk indexers should not be syslog receivers, and sending syslog directly to indexers causes data loss and instability. Indexers are not optimized for packet-level syslog ingestion. Also, syslog cannot load-balance in a Splunk-aware manner.

B. Use a Splunk indexer to collect a network input on port 514 directly.
Indexers listening on 514 violates Splunk best practices. Splunk processes are not designed for high-volume syslog receipt, and port 514 requires root privileges. This approach risks dropped events and performance degradation.

C. Use a Splunk forwarder to collect the input on port 514 and forward the data.
Although better than sending directly to indexers, Splunk forwarders are still not ideal syslog servers. They lack the robust queuing and buffering capabilities of true syslog daemons and should not be used as primary syslog listeners.

Explanation:

When converting a CSV lookup to a KV Store lookup in Splunk, it is important to understand how KV Store collections behave in a Search Head Cluster (SHC) environment.

KV Store collections are defined in collections.conf. By default, these collections are local to the search head where they are created and are not automatically shared across other search head cluster members.

To make a KV Store collection available across all search heads for automatic lookups, you must enable replication using the replicate=true attribute in collections.conf.

Why replicate=true is correct
Setting replicate=true ensures that the KV Store collection is replicated across all members of the Search Head Cluster using the KV Store replication mechanism.

Once replication is enabled, any search head in the cluster can access the same KV Store data, making automatic lookups consistent and available cluster-wide.

Important note: The KV Store collection must also be included in an app that is deployed to all search heads in the cluster to ensure consistency.

Why the other options are incorrect
A. Add repFactor=true attribute in collections.conf
This is incorrect because repFactor is not a valid attribute in collections.conf. The correct attribute for KV Store replication is replicate.

Also, repFactor is used in indexer clustering (for replication factor settings), not KV Store configurations.

B. Add replicate=true attribute in lookups.conf
This is incorrect because lookups.conf defines lookup definitions (such as CSV or KV Store lookup definitions), but replication behavior is controlled in collections.conf, not in lookup definitions.

Therefore, placing replicate=true in lookups.conf has no effect on KV Store replication.

D. Add repFactor=true attribute in lookups.conf
This option is incorrect for two reasons: it uses the wrong file (lookups.conf) and the wrong attribute name (repFactor, which is not valid for KV Store or lookups).

Reference
Splunk Docs – Configure KV Store replication
Splunk Docs – collections.conf specification (replicate attribute)
SPLK-2002 Exam Guide – KV Store replication in SHC environments