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:
The deployer in a Search Head Cluster operates on a "pull" model, not a "push" model. Let's break down the correct statement and why the others are incorrect.

Why A is Correct:
The SHC deployer acts as a central repository for apps and configurations (the "bundle"). The individual search head cluster members are configured to periodically "phone home" to the deployer. During this check-in, they ask, "Is there a new bundle for me?" If there is, the member pulls it down and applies it locally. This design is crucial for the autonomy and stability of the cluster, as it prevents the deployer from forcefully pushing a potentially broken configuration to all members simultaneously.
Key Concept: Pull-based distribution.

Why the Other Options Are Incorrect:

B. The deployer must be used to distribute non-replicable configurations to search head cluster members.
Incorrect: This statement is backwards. The deployer is specifically used for configurations that are replicable (apps, knowledge objects, saved searches). "Non-replicable" configurations (like server.conf settings unique to each member, such as its hostname) must be managed locally on each search head member and are explicitly excluded from the deployer bundle.

C. The deployer must distribute configurations to search head cluster members to be valid configurations.
Incorrect: This is too absolute and misleading. While the deployer is the recommended and primary method for distributing shared configurations, it is not the only way configurations become valid. A member can have locally applied configurations that are valid for its own operation. Furthermore, the statement implies a dependency that doesn't exist; a configuration's validity is not contingent on the deployer distributing it.

D. The deployer only distributes configurations to search head cluster members with splunk apply shcluster-bundle.
Incorrect: This command is run on the deployer, not on the members. Its purpose is to finalize the creation of a new app bundle on the deployer (e.g., after you have copied an app into the $SPLUNK_HOME/etc/shcluster/apps/ directory). Running splunk apply shcluster-bundle on the deployer makes the new bundle available. It is then the responsibility of the individual SHC members to "phone home" and pull this new bundle down. The command does not actively "distribute" or "push" the bundle to the members.

Reference
This process is documented in the official Splunk Enterprise documentation, particularly in the "Distribute configurations to the search head cluster" section. The documentation states:
"The deployer does not push the configuration bundle to the members. Instead, each member periodically polls the deployer to see whether a new bundle is available. If a new bundle is available, the member restarts and loads the new bundle."
This clearly describes the "phone home" (polling) mechanism, confirming that option A is the correct and accurate description of a good practice.

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:
In Splunk deployment planning, the stage where you determine who is authorized to view specific data, including sensitive network data, is the data policy definition phase. Policies are the formal rules that govern data visibility, retention, access control, and compliance requirements. They ensure Splunk aligns with organizational security standards, regulatory frameworks, and internal governance.

A deployment plan is not just about technical topology; it also requires clear rules for data governance. Splunk environments often ingest highly sensitive information such as firewall logs, authentication events, and network traffic metadata. Without a defined policy, there is risk of unauthorized access, regulatory violations, or exposure of confidential information. Therefore, the correct answer is D. Data policy definition, because this is the point in planning where access rights and visibility are explicitly defined.

Why the other options are not correct

Option A: Deployment schedule A deployment schedule defines when Splunk components (indexers, search heads, forwarders, etc.) will be rolled out. It is a timeline artifact, not a governance artifact. While important for project management, it does not address who can see data. Access control decisions are not part of scheduling.

Option B: Topology diagramming Topology diagrams illustrate how Splunk components are arranged across the IT environment. They show current and future architecture, clustering, and scaling strategies. However, they do not define who has permission to view specific datasets. Topology is structural, not policy‑driven.

Option C: Data source inventory A data source inventory catalogs what data sources exist (e.g., firewalls, routers, application logs). It is critical for onboarding and ingestion planning, but it does not define who can access those logs once ingested. Inventory is descriptive, not prescriptive.

Option D: Data policy definition Correct, because this is the step where Splunk architects define data ownership, access rights, retention rules, and compliance boundaries. Policies ensure that sensitive network data is only visible to authorized roles (e.g., security analysts, compliance officers) and not exposed to general users.

References:
Splunk Docs – Deployment Planning
Splunk Security Overview – Data Governance

Explanation:
In a Splunk search head cluster (SHC), the captain is the elected member responsible for coordinating cluster activities. The captain manages scheduled searches, distributes knowledge objects, and ensures consistency across the cluster. Because of this, the captain can become a bottleneck if it is also burdened with executing scheduled searches.

To reduce the captain’s workload, Splunk provides a configuration setting:
captain_is_adhoc_searchhead = true This setting ensures that the captain only runs ad‑hoc searches (interactive queries initiated by users) and does not execute scheduled searches. By applying this setting only on the current captain, you offload scheduled searches to other search head members, reducing the captain’s overhead and improving cluster stability.
Thus, the correct answer is D. captain_is_adhoc_searchhead = true (on the current captain).

Why the Other Options Are Incorrect

A. adhoc_searchhead = true (on all members)
This option is misleading. The adhoc_searchhead setting is not the correct parameter for controlling captain workload. It is not used in Splunk’s search head cluster configuration for scheduled search distribution. Applying this across all members would not achieve the intended effect.

B. adhoc_searchhead = true (on the current captain)
Again, incorrect because adhoc_searchhead is not the right setting. The exam tests whether you know the distinction between adhoc_searchhead (not relevant here) and captain_is_adhoc_searchhead (the correct parameter).

C. captain_is_adhoc_searchhead = true (on all members)
This would incorrectly configure all members to act as ad‑hoc search heads only, preventing scheduled searches from running anywhere in the cluster. That would break functionality, as scheduled searches must run on non‑captain members. The setting is intended only for the captain, not for all members.

D. captain_is_adhoc_searchhead = true (on the current captain) Correct.
This ensures the captain does not run scheduled searches, reducing its workload while allowing other members to handle scheduled jobs.

Operational Insight
Splunk’s design philosophy for SHC is to keep the captain focused on coordination and management tasks. Scheduled searches can be resource‑intensive, and if the captain is overloaded, it may fail to properly manage cluster activities such as knowledge object replication or search scheduling. By restricting the captain to ad‑hoc searches only, you ensure:

Improved stability: The captain remains responsive for cluster coordination.

Balanced workload: Scheduled searches are distributed across non‑captain members.

High availability: If the captain fails, another member is elected, and the setting applies to the new captain.

This is a best practice in large Splunk deployments where scheduled searches are numerous and resource‑heavy.

References
Splunk Docs – Search Head Clustering
Splunk Admin Manual – Distributed Search

Explanation:

Correct Answer:
A. SPLUNK_HOME/etc/system/local
When configurations are pushed from the deployer to a search head cluster (SHC) member, Splunk merges them into the system/local directory. This location is the highest precedence layer in Splunk’s configuration hierarchy. The deployer’s role is to distribute app and configuration bundles across all SHC members, ensuring consistency. Once delivered, the bundle is unpacked and merged into etc/system/local on each member, overriding lower‑precedence settings.

This behavior is critical because search head clustering requires uniformity across members. By merging into system/local, Splunk guarantees that deployer‑pushed settings take precedence over app defaults or local overrides, maintaining cluster stability and predictable behavior.

Why the Other Options Are Incorrect

B. SPLUNK_HOME/etc/apps/APP_HOME/local
This directory holds local app‑specific overrides created manually on a single search head. These are not touched by the deployer. If deployer bundles were merged here, they could overwrite administrator‑specific local customizations, which Splunk explicitly avoids. Thus, deployer merges bypass app/local directories.

C. SPLUNK_HOME/etc/apps/search/default
Default directories contain vendor‑supplied baseline configurations for apps. They are static and never overwritten by deployer pushes. The deployer does not merge into default directories because defaults are meant to remain pristine, serving as the fallback baseline.

D. SPLUNK_HOME/etc/apps/APP_HOME/default
Same reasoning as option C. Default directories are immutable baselines. Deployer bundles are not merged here, as doing so would break the principle of configuration layering. Defaults always remain untouched, while deployer merges occur at the system/local layer.

References
Splunk Docs: Search Head Cluster: Deploy configurations — confirms deployer bundles are merged into system/local.

Splunk Docs: Configuration file precedence — explains the hierarchy of default vs local vs system/local.

Splunk Admin Guide: Manage app deployment in SHC — details how deployer pushes bundles and where they land.

Explanation:
Index‑time attributes are those that affect how raw data is broken into events during ingestion. These directly influence indexing performance because Splunk must parse and segment incoming data before storing it.

LINE_BREAKER
Defines the regular expression Splunk uses to split raw data into events.
Complex or inefficient regex patterns here can slow down parsing and indexing throughput.
Since event boundaries are determined at index time, this attribute has a direct impact on performance.

SHOULD_LINEMERGE

Controls whether Splunk should attempt to merge multiple lines into a single event.
If set to true, Splunk must perform additional processing to evaluate line merging, which can degrade indexing speed.
Best practice is to set SHOULD_LINEMERGE=false for structured data (like JSON or CSV) to improve performance.

Why the other options are incorrect

A. REPORT
Used to apply field extractions via transforms.
These are search‑time operations, not index‑time, so they do not affect indexing performance.

C. ANNOTATE_PUNCT
Adds punctuation annotations to assist automatic field discovery.
This is a search‑time setting, not evaluated during indexing.

Key Exam Point
Only index‑time attributes affect ingestion speed. Most props.conf attributes are search‑time, meaning they apply when data is queried. For SPLK‑2002, remember: LINE_BREAKER and SHOULD_LINEMERGE are the two props.conf attributes that impact indexing performance.

References
Splunk Docs: Configure event line breaking — explains LINE_BREAKER and its role in event segmentation.

Splunk Docs: SHOULD_LINEMERGE attribute — details how line merging affects indexing.

Splunk Docs: Index-time vs search-time operations — clarifies which attributes impact ingestion vs search.

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:
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

Explanation:
Splunk’s deployment architecture includes two key roles: the deployment server (DS) and the deployment client (DC). The deployment server manages configurations and apps, while deployment clients (such as Universal Forwarders) connect to the DS to receive updates. When troubleshooting or monitoring deployment activity, administrators often need to search Splunk’s internal logs to see messages generated by the deployment client.
The correct search for deployment client messages from a Universal Forwarder is:

This search works because:

index=_internal: Splunk logs its own operational activity in the _internal index. This includes forwarder activity, deployment communications, and component status. The _audit index, by contrast, is reserved for audit logs of user actions, not system components.
component=DC*: The component field identifies the source subsystem. DC* refers to deployment client logs. This is the key filter to isolate messages from the client side.
host=: Restricts results to the Universal Forwarder host, ensuring you are looking at client messages rather than deployment server logs.
stats count by message: Aggregates the messages for analysis, making it easier to see what types of deployment client messages are occurring.
This search is exam‑relevant because it tests your ability to distinguish between deployment client vs deployment server logs, and between _internal vs _audit indexes.

Why the Other Options Are Incorrect

A. index=_audit component=DC* host=
_audit index contains audit logs such as user role changes, search activity, and authentication events. It does not store deployment client messages.
The host filter points to the deployment server, not the client. This option is doubly incorrect: wrong index and wrong host.

B. index=_audit component=DC* host=
Although the host filter is correct (), the index is wrong. _audit does not contain deployment client logs. Only _internal holds those messages.

D. index=_internal component=DS* host=
This search looks at deployment server (DS*) messages, not deployment client (DC*).
While _internal is the right index, the component filter is wrong. This would show server activity, not client messages.

Operational Mapping
In practice, administrators use searches like option C to verify that Universal Forwarders are connecting properly to the deployment server and receiving configurations. For example, if a forwarder is not updating, you can check _internal logs with component=DC* to see if there are connection errors or bundle download failures.
Option D would be used when troubleshooting the deployment server itself, such as verifying that it is pushing bundles correctly. Options A and B are distractors because _audit is unrelated to deployment communications.

Exam Relevance
This question is a classic SPLK‑2002 exam trap. Candidates often confuse _audit with _internal, or mix up DS vs DC components. The exam expects you to know:
Deployment client logs → _internal, component=DC*
Deployment server logs → _internal, component=DS*
Audit logs → _audit, unrelated to deployment messaging
Memorizing this distinction helps eliminate distractors quickly.

References
Splunk Docs: Monitor deployment clients
Splunk Docs: Deployment server overview

Explanation:
Splunk Enterprise performance depends on how resources are allocated between search heads and indexers (search peers). This exam question is designed to test your understanding of distributed search architecture and hardware scaling.

B. Adding RAM to an existing search head provides additional search capacity ✅
This is true. Search heads are responsible for managing search jobs, merging results from indexers, and handling user interactions. Their performance is heavily dependent on available memory.
More RAM allows the search head to manage more concurrent searches.
Larger result sets can be cached in memory.
Responsiveness improves when multiple users run searches simultaneously.
Splunk documentation emphasizes that memory is a critical resource for search heads. Increasing RAM directly increases search capacity.

D. Adding search heads provides additional CPU cores to run more concurrent searches ✅
This is also true. Each search head brings its own CPU resources. By adding search heads, you increase the number of CPU cores available across the cluster, which allows more concurrent searches to be executed.
Search head clustering distributes user search requests across multiple search heads.
More search heads = more CPU cores = higher concurrency capacity.
This improves scalability for environments with many users running searches simultaneously.
It’s important to note that CPU cores are not pooled across search heads for a single search, but concurrency across multiple searches is improved.

Why the Other Options Are Incorrect

A. Adding search peers increases the maximum size of search results ❌
Incorrect. The maximum size of search results is not determined by the number of search peers. Search peers distribute indexing and search workloads, but they do not change the maximum result size. That limit is controlled by search head memory and configuration parameters such as maxresultrows.

C. Adding search peers increases the search throughput as search load increases ❌
Misleading. Adding search peers (indexers) does improve indexing throughput and distributes search execution, but search throughput is primarily constrained by the search head’s ability to manage jobs and by query efficiency. Simply adding peers does not guarantee higher throughput unless the bottleneck is indexing capacity. In exam context, this option is considered incorrect because Splunk stresses that search head resources (RAM/CPU) are the limiting factor for search capacity.

Exam Relevance
This is a common SPLK‑2002 exam trap. Candidates often confuse scaling indexers with scaling search heads. The exam expects you to recognize that search head resources (RAM/CPU) are the limiting factor for search capacity.

References
Splunk Docs: Distributed search overview
Splunk Docs: Search head clustering

Explanation:
Splunk loads configuration files in a specific order, where settings from directories with higher precedence override those from directories with lower precedence. The standard precedence order (from highest to lowest) is:
User-defined preferences (from the user interface)
App local directories (within a specific app)
App default directories (within a specific app)
System local directory ($SPLUNK_HOME/etc/system/local/)
App local directories (from all apps, in alphabetical order)
App default directories (from all apps, in alphabetical order)
System default directory ($SPLUNK_HOME/etc/system/default/)

Let's break down why A is correct and the others are not, based on this hierarchy:

A. System local directory (Correct):
The $SPLUNK_HOME/etc/system/local/ directory has the highest precedence among the directories listed in the options. A setting defined here will override the same setting in any app's local or default directory, as well as the system default directory. It is the recommended place for administrators to make system-wide customizations that should not be overridden by app updates.

B. System default directory (Incorrect):
The $SPLUNK_HOME/etc/system/default/ directory has the lowest precedence. These files contain the factory-default settings for Splunk. Any setting here will be overridden by a setting for the same stanza and key in any other directory, including all app directories and the system local directory.

C. App local directories, in ASCII order (Incorrect):
While settings in an app's local directory ($SPLUNK_HOME/etc/apps//local/) have high precedence (overriding the app's own default directory and the system default directory), they are still overridden by the System local directory. The ASCII ordering only matters for resolving conflicts between different apps at the same precedence level.

D. App default directories, in ASCII order (Incorrect):
App default directories ($SPLUNK_HOME/etc/apps//default/) have very low precedence. They are intended for an app's initial configuration and are overridden by the app's own local directory, the system local directory, and even the local directories of other apps.

Reference:
Splunk Documentation: "Configuration file precedence"