Splunk Enterprise Certified Architect - SPLK-2002 Exam Dumps

Explanation:
When Splunk indexes data, it stores the original, raw event data and creates essential metadata files that enable fast searching. The data is physically organized into buckets on the indexer's disk. Inside each bucket, Splunk creates two primary types of files: the raw data files (which hold the compressed, original log events) and the index files (which contain the metadata mapping where to find those events, known as Time Series Index or .tsidx files). These two file types, often referred to simply as the index and raw data, are fundamental to Splunk's efficient storage and search process.

Correct Option:

A. Rawdata and .tsidx files.
Rawdata (Index files): This is the compressed, journaled file that contains the original, immutable raw event data. This is what Splunk reads to display the actual log message during a search.

tsidx files: These are the Time Series Index files. They store the metadata required for efficient searching, including:

Pointers to the raw data location.

Event timestamps.

A list of all unique terms (words/tokens) found in the data, along with their location and frequency. This is what allows Splunk to skip large amounts of data and directly jump to relevant events, making searches extremely fast.

Incorrect Options:

B. Rawdata and index files.
This is a tricky option. While it's generally correct to say Splunk creates "rawdata" and "index files," the more technically precise Splunk term for the index component that stores all terms and pointers is the .tsidx file. Option A uses the more specific term required for an architect-level exam.

C. Compressed and .tsidx files.
While true that both components are compressed and .tsidx is created, this option is incomplete as it misses the raw data component. The raw data itself is compressed

D. Compressed and meta data files.
This is too generic. While the files are compressed and the .tsidx files are metadata, the terms are not specific enough to describe the two mandatory and unique file structures (Rawdata and .tsidx) that make up a Splunk bucket.

Reference:
Splunk Documentation: How Splunk Enterprise stores data and Buckets and index files Concept: Splunk Bucket Architecture (The core structure containing the raw data and the .tsidx index files).

Explanation:
Splunk Enterprise Security (ES) relies heavily on Data Model acceleration to power its correlation searches, investigations, and dashboards. Accelerating a Data Model pre-computes and stores the results of its defined data structures, which allows for very fast query performance. This process creates a significant amount of additional data on the indexers, which is stored in special accelerated summary indexes and TSIDX (time-series index) files, directly increasing storage requirements.

Correct Option:

D. The number of Data Models accelerated:
This is the most significant factor. Each accelerated Data Model generates its own set of summary index data and tsidx files. Enterprise Security accelerates several critical Data Models by default (like Intrusion_Domain, Malware_Attacks, Authentication). The more Data Models that are accelerated, and the longer their acceleration time windows, the more storage capacity is consumed.

Incorrect Options:

A. The number of scheduled (correlation) searches:
While a high number of searches consumes CPU and memory on the search head, their impact on storage is indirect and minimal compared to Data Model acceleration. The searches themselves and their results do not typically create large, persistent data stores.

B. The number of Splunk users configured:
The number of users has a negligible direct impact on storage sizing. User accounts and preferences require minimal metadata storage. The primary data volume comes from ingested events and accelerated data, not the user count.

C. The number of source types used in the environment:
The diversity of source types affects parsing and data structure but does not inherently dictate storage size. Storage is driven by the volume of data ingested and the amount of data generated by acceleration processes, not merely the number of different source types.

Reference:
Splunk Enterprise Security Admin Manual: "Size your Splunk Enterprise Security deployment". The documentation explicitly states that the storage requirements for ES are heavily influenced by Data Model acceleration and provides specific guidance on calculating the additional storage needed based on the number and scope of accelerated data models.

Explanation:
In a search head cluster, the KV store uses a distributed architecture where all search heads can read and write to the KV store. Although internally there is a KV store primary node for replication and consistency, administrators and users do not need to target it directly. Any search head in the cluster can update KV store collections because changes are automatically synchronized across the cluster.

Correct Option:

D. Any search head in the cluster
All SHC members act as valid access points for KV store operations.

Updates are automatically distributed through the KV store replication mechanism.

Users or apps do not need to know which node is primary; the cluster handles consistency internally.

Incorrect Options:

A. The search head cluster captain
The captain coordinates searches and knowledge objects, but does not serve as a unique update point for KV store.

KV store updates do not depend on the captain role.

B. The KV store primary search head
Although a primary exists internally, Splunk does not require admins to target it.

Updates can be made from any SHC member; the primary manages replication only behind the scenes.

C. Any search head except the captain
Incorrect because all search heads—including the captain—can update KV store data.

The captain does not restrict KV store writes.

Reference:
Splunk Docs: KV Store in Search Head Clusters

Explanation:
In a Search Head Cluster (SHC), the Deployer is responsible for distributing consistent configurations to all cluster members. When preparing the configuration bundle from the apps stored in the Deployer's master app directory, the Deployer follows Splunk's configuration file precedence. This rule states that the contents of the local directory override the contents of the default directory. Therefore, the Deployer merges the settings from both default and local directories into a single, effective set of configuration files before packaging and pushing the bundle to the SHC members. This ensures that custom settings are consistently applied across the cluster.

Correct Option:

B. The local folder is merged into the default folder and deployed to the search heads.
Merging: The Deployer's bundle-building process combines the settings found in $SPLUNK_HOME/etc/deployment-apps//default and $SPLUNK_HOME/etc/deployment-apps//local on the Deployer. The local settings take precedence during this merge.

Deployment: The resulting merged configuration files (the effective settings) are then packaged into the deployment bundle and pushed to the search head cluster members' configuration location (etc/apps). The individual default and local folders, as they exist on the Deployer, are not typically copied directly; rather, their consolidated content is deployed to ensure consistency.

Incorrect Options:

A. The local folder is copied to the local folder on the search heads.
This is incorrect. While this is how configurations are applied locally on a single instance, the Deployer generally manages the entire effective configuration. Allowing individual search heads to maintain their own local directories would override the Deployer's centralized configuration and lead to inconsistency across the cluster. The Deployer's bundle push is designed to overwrite local changes to enforce uniformity.

C. Only certain .conf files in the local folder are deployed to the search heads.
This is partially true in that the merged result contains all necessary configurations, but the process is one of merging all content from local and default before pushing. This option doesn't fully describe the required merge operation for precedence.

D. The local folder is ignored and only the default folder is copied to the search heads.
This is incorrect. If the local folder were ignored, all customizations made to the app (e.g., changed port numbers, custom data inputs, modified field extractions) would be lost upon deployment, rendering the app unusable or reverting it to its factory default state.

Reference:
Splunk Documentation: Deploy a configuration bundle and Configuration file precedence Concept: Deployer Bundle Creation and Configuration Consistency in SHC.

Explanation:
While high availability is the primary benefit, a search head cluster (SHC) provides significant operational advantages by synchronizing configurations across all member nodes. This synchronization ensures a consistent user experience and simplifies administration. The cluster automatically manages the distribution of knowledge objects, which are the core assets for searching and analyzing data, eliminating the need for manual replication.

Correct Option:

D. Automatic replication of user knowledge objects:
This is a key benefit. When a user creates or edits a knowledge object (such as a saved search, dashboard, or lookup) on one search head cluster member, the SHC's internal replication mechanism automatically propagates that change to all other members in the cluster. This ensures configuration consistency and provides a unified user experience.

Incorrect Options:

A. Allows indexers to maintain multiple searchable copies of all data:
This is a function of an indexer cluster, not a search head cluster. The replication factor and search factor are configured on the indexer cluster manager to control data copies. A search head cluster queries the indexers but does not control how they store data.

B. Input settings are synchronized between search heads:
Search heads are primarily for searching, not data ingestion. Input configurations (defined in inputs.conf) are not synchronized across a search head cluster. Distributing input configurations is the role of a deployment server.

C. Fewer network ports are required to be opened between search heads:
This is incorrect. Forming a search head cluster actually requires specific network ports to be opened between all member nodes to facilitate communication, replication, and captain election. It does not reduce the number of required open ports.

Reference:
Splunk Enterprise Admin Manual: "About search head clustering". The documentation lists the benefits, which include the automatic replication of knowledge objects (like saved searches and lookups) across all cluster members to maintain synchronization, in addition to providing high availability.

Explanation:
Intermediate forwarders (often Heavy Forwarders or Universal Forwarders acting as aggregation points) should be avoided when possible primarily to eliminate potential performance bottlenecks and preserve the integrity of distributed search. By funneling many data streams into a few intermediate forwarders, you create an artificial bottleneck.

This bottleneck increases latency (time from event creation to search availability) because data queues up at the intermediate tier.

It causes uneven data distribution (hotspots) on the indexers, as the data stream concentrates into a few connections.

The uneven distribution harms search performance because searches cannot effectively use the full parallel processing power of the entire indexer cluster (map-reduce is minimized).

Correct Option:

D. To eliminate potential performance bottlenecks.
Bottleneck: Intermediate forwarders become single points of congestion where the high volume of traffic from all source forwarders aggregates. This leads to queue buildup, causing latency in data ingestion.

Search Degradation: They concentrate the data stream, resulting in fewer total connections to the indexer cluster than if all source forwarders connected directly. This creates data imbalance (hotspots) on the indexers, which drastically reduces the parallelization effectiveness of searches, leading to poor search performance and user experience.

Incorrect Options:

A. To minimize license usage and cost.
This is generally false. Filtering data on an intermediate forwarder (which requires a Heavy Forwarder) can reduce data volume sent to indexers, which saves license usage. However, filtering data on the indexer is also non-license-consuming and is the recommended best practice. Intermediate forwarders are avoided for performance and complexity, not license management.

B. To decrease mean time between failures.
This is incorrect. Adding an extra tier (the intermediate forwarders) to the data path actually increases the complexity and increases the number of components that can fail, thereby increasing the mean time between failures (MTBF), or more accurately, increasing the mean time to repair (MTTR) and increasing the likelihood of failure.

C. Because intermediate forwarders cannot be managed by a deployment server.
This is false. Intermediate forwarders are standard Splunk instances (UF or HF) and can be managed by a Deployment Server just like any other forwarder or indexer. Management complexity increases, but they are not inherently unmanageable.

Reference:
Splunk Documentation: Intermediate data routing using forwarders and Universal or Heavy, that is the question? Concept: Forwarder Best Practices (The principle is to forward data directly to indexers to achieve optimal load balancing and search-time performance).

Explanation:
The deployer is a dedicated instance used to distribute apps and configurations to all members of a search head cluster (SHC). It does not send apps to individual search heads directly. Instead, it pushes a single, consolidated app bundle to a shared location that is then replicated to every member of the SHC. This ensures all search heads have identical configurations, which is critical for the cluster to function correctly.

Correct Option:

D. etc/shcluster/apps:
This is the correct default target directory on the deployer itself. When you place an app in $SPLUNK_HOME/etc/shcluster/apps/ on the deployer and run the apply shcluster-bundle command, the deployer packages the apps and pushes them to the search head cluster captain. The captain then distributes the bundle to all cluster members, who unpack it into their local etc/apps directories.

Incorrect Options:

A. etc/slave-apps//default:
The term "slave-apps" is outdated and not used in modern Splunk architecture for this purpose. This path is not the standard target for the deployer's app distribution.

B. etc/deploy-apps//default:
This is not a standard directory used by any core Splunk component. While descriptive, it is a distractor and does not exist in the default Splunk file structure for this function.

C. etc/apps//default:
This is the standard local directory on any Splunk instance (including a search head cluster member) where apps are stored and executed. However, the deployer does not send apps directly to this location on the cluster members. The apps are pushed via the cluster's replication mechanism and end up here, but the deployer's own source directory is etc/shcluster/apps.

Reference:
Splunk Enterprise Admin Manual: "How to use the deployer to distribute apps and configurations". The documentation specifies that on the deployer, apps must be placed in the $SPLUNK_HOME/etc/shcluster/apps directory to be distributed to a search head cluster.

Explanation:
A multisite indexer cluster in Splunk is managed by a single cluster manager (formerly master node), regardless of how many sites exist. The cluster manager oversees bucket replication, peer membership, and configuration distribution for the entire cluster. Splunk’s clustering architecture is centralized, meaning multiple cluster managers would cause conflicts and inconsistency. Thus, only one cluster manager is required and supported for the entire multisite deployment.

Correct Option:

C. One for the entire cluster
Splunk supports only a single cluster manager per indexer cluster (single-site or multisite).

It centrally controls replication, bucket fix-ups, and configuration for all sites.

Additional cluster managers are not supported and would break consensus and cluster stability.

Incorrect Options:

A. Two for the entire cluster
Splunk does not support configuring multiple active cluster managers.

HA is achieved using OS-level or hypervisor-level failover—not multiple cluster managers.

B. One for each site
A multisite cluster still functions as one cluster, not multiple clusters.

Having one cluster manager per site is unsupported and would fragment cluster control.

D. Two for each site
No Splunk architecture supports multiple cluster managers per site.

This would cause conflicting bucket management and is not part of Splunk cluster architecture.

Reference:
Splunk Docs: About indexer cluster architecture

Explanation:
Before designing and deploying a Splunk environment, an Architect must gather requirements to properly size the infrastructure (indexers, search heads, forwarders) and configure the system (indexes, user roles). This process requires understanding what data is coming in, who is using the system, and why they need it. The overall goals, the key users, and the data sources are all direct inputs to the architecture and sizing process.

Correct Options:

B. Overall goals for the deployment.
Impact: The goals dictate the required use cases (e.g., security monitoring, IT operations, business analytics) and determine which premium apps (like ES or ITSI) are needed. This directly influences the architectural complexity (e.g., separate search heads for different functions), the performance required, and the necessary knowledge objects (CIM, data models).

C. Key users.
Impact: Knowing the user profile is critical for sizing the search head layer. This includes:

Number of concurrent users: Directly affects the required CPU cores and RAM on the search heads and indexers.

User roles/permissions (RBAC): Dictates the initial security configuration and Role-Based Access Control setup.

Search patterns: Identifies power users, scheduled reports, and real-time search needs, all of which stress the system differently.

D. Data sources.
Impact: This information is the foundation of the entire deployment plan and is necessary for sizing the indexer layer and obtaining the license:

Type of data (logs, metrics, events): Determines required Technology Add-ons (TAs) and index configuration.

Volume (GB/day): Used to calculate the license size and the required storage capacity (hot/warm/cold/frozen) and IOPS for the indexers.

Data ingestion rate (Events/sec): Used for forwarder capacity planning and ensuring the network and indexers can handle peak load.

Incorrect Option:

A. List of vendors for network devices.
While knowing the network device types is essential for data collection (e.g., "Cisco IOS" vs "Juniper"), a specific list of vendors is generally not a primary architectural prerequisite for the core Splunk infrastructure sizing (indexers, search heads) or initial license consumption. The data type and volume are more important than the brand name. The vendor list becomes critical after the initial infrastructure is planned, when selecting the correct Technology Add-ons (TAs) for data ingestion and CIM compliance.

Reference:
Splunk Documentation: Splunk Deployment Planning Process Concept: Requirements Gathering for Sizing and Architecture (Architecting Splunk Enterprise Deployments course material).

Explanation:
This question tests the understanding of role compatibility in a Splunk deployment. The deployer is a dedicated instance that distributes apps and configurations to a search head cluster (SHC). It must be a standalone instance to function correctly. Sharing its functionality with another role means installing it on a server that already performs one of the other listed functions, which can lead to conflicts, especially concerning configuration management and resource allocation.

Correct Option:

B. License master:
This is the instance that cannot share functionality with the deployer. The license master has a critical, centralized role in managing the license pool for the entire deployment. Co-locating the deployer on the license master is unsupported because both are central management points, and combining them can lead to resource contention and configuration conflicts. Splunk's best practices dictate that the deployer be a standalone instance.

Incorrect Options:

A. Search head cluster member:
The deployer must not be a member of the search head cluster it serves. The deployer pushes content to the SHC members; if it were a member itself, it would create a circular dependency and configuration conflict. This is a well-known restriction, but the question asks which instance cannot share functionality, and the license master is the more fundamentally incompatible role.

C. Master node:
The cluster manager (master node) for an indexer cluster can technically be co-located with a deployer. However, this is generally not recommended for production environments due to the potential resource strain and the critical nature of both roles. It is, however, a more common co-location in lab or small environments than with a license master.

D. Monitoring Console (MC):
The Monitoring Console is a web-based application, not a separate instance type. It can be accessed from various Splunk instances, including a standalone deployer, a search head, or a cluster manager. Therefore, the MC's functionality is not mutually exclusive with the deployer role.

Reference:
Splunk Enterprise Admin Manual: "Deployer overview". The documentation specifies that the deployer must be a separate instance and should not be installed on a cluster manager, license master, or search head. The license master is explicitly called out as an incompatible role due to its critical licensing function.