SPLK-2002 Exam Dumps - Splunk Enterprise Certified Architect Questions and Answers

When building a deployment plan, the architect should perform the following tasks:

Use case checklist. A use case checklist is a document that lists the use cases that the deployment will support, along with the data sources, the data volume, the data retention, the data model, the dashboards, the reports, the alerts, and the roles and permissions for each use case. A use case checklist helps to define the scope and the functionality of the deployment, and to identify the dependencies and the requirements for each use case1

Inventory data sources. An inventory of data sources is a document that lists the data sources that the deployment will ingest, along with the data type, the data format, the data location, the data collection method, the data volume, the data frequency, and the data owner for each data source. An inventory of data sources helps to determine the data ingestion strategy, the data parsing and enrichment, the data storage and retention, and the data security and compliance for the deployment1

Review network topology. A review of network topology is a process that examines the network infrastructure and the network connectivity of the deployment, along with the network bandwidth, the network latency, the network security, and the network monitoring for the deployment. A review of network topology helps to optimize the network performance and reliability, and to identify the network risks and mitigations for the deployment1

Installing Splunk apps is not a task that the architect should perform when building a deployment plan, as it is a task that the administrator should perform when implementing the deployment plan. Installing Splunk apps is a technical activity that requires access to the Splunk instances and the Splunk configurations, which are not available at the planning stage

The best practice for ingesting syslog data from network devices on port 514 into Splunk is to configure syslog to write logs and use a Splunk forwarder to collect the logs. This practice will ensure that the data is reliably collected and forwarded to Splunk, without losing any data or overloading the Splunk indexer. Configuring syslog to send the data to multiple Splunk indexers will not guarantee data reliability, as syslog is a UDP protocol that does not provide acknowledgment or delivery confirmation. Using a Splunk indexer to collect a network input on port 514 directly will not provide data reliability or load balancing, as the indexer may not be able to handle the incoming data volume or distribute it to other indexers. Using a Splunk forwarder to collect the input on port 514 and forward the data will not provide data reliability, as the forwarder may not be able to receive the data from syslog or buffer it in case of network issues. For more information, see [Get data from TCP and UDP ports] and [Best practices for syslog data] in the Splunk documentation.

The replication factor and the search factor are two important settings for a Splunk indexer cluster. The replication factor determines how many copies of each bucket are maintained across the set of peer nodes. The search factor determines how many searchable copies of each bucket are maintained. The default values for both settings are 3, which means that each bucket has three copies, and at least one of them is searchable

The Search Job Inspector can be used to expand the following sections: Search job properties and Optimization suggestions. The Search Job Inspector is a tool that provides detailed information about a search job, such as the search parameters, the search statistics, the search timeline, and the search log. The Search Job Inspector can be accessed by clicking the Job menu in the Search bar and selecting Inspect Job. The Search Job Inspector has several sections that can be expanded or collapsed by clicking the arrow icon next to the section name. The Search job properties section shows the basic information about the search job, such as the SID, the status, the duration, the disk usage, and the scan count. The Optimization suggestions section shows the suggestions for improving the search performance, such as using transforming commands, filtering events, or reducing fields. The Execution costs and Saved search history sections are not part of the Search Job Inspector, and they cannot be expanded. The Execution costs section is part of the Search Dashboard, which shows the relative costs of each search component, such as commands, lookups, or subsearches. The Saved search history section is part of the Saved Searches page, which shows the history of the saved searches that have been run by the user or by a schedule

Comprehensive and Detailed Explanation (From Splunk Enterprise Documentation)Splunk Enterprise documentation clarifies that none of the listed configurations are prohibited in production. Splunk allows the Cluster Manager to be colocated with the Monitoring Console in small deployments because both are management-plane functions and do not handle ingestion or search traffic. The documentation also states that the Search Head Cluster Deployer is not a runtime component and has minimal performance requirements, so it may be colocated with the Monitoring Console or Licensing Master when hardware resources permit.

Splunk also supports virtual machines for both search heads and indexers, provided they are deployed with dedicated CPU, storage throughput, and predictable performance. Splunk’s official hardware guidance specifies that while bare metal often yields higher performance, virtualized deployments are fully supported in production as long as sizing principles are met.

Because Splunk explicitly supports all four configurations under proper sizing and best-practice guidelines, there is no correct selection for “unsupported.” The question is outdated relative to current Splunk Enterprise recommendations.

According to the Splunk SmartStore Architecture Guide, the primary benefit of SmartStore is the separation of storage from compute resources within an indexer cluster. SmartStore enables Splunk to decouple indexer storage (data at rest) from the compute layer (indexers that perform searches and indexing).

With SmartStore, active (hot/warm) data remains on local disk for fast access, while older, less frequently searched (remote) data is stored in an external object storage system such as Amazon S3, Google Cloud Storage, or on-premises S3-compatible storage. This separation reduces the storage footprint on indexers, allowing organizations to scale compute and storage independently.

This architecture improves cost efficiency and scalability by:

Lowering on-premises storage costs using object storage for retention.

Enabling dynamic scaling of indexers without impacting total data availability.

Reducing replication overhead since SmartStore manages data objects efficiently.

SmartStore does not affect replication or search factors (Option A), does not handle Knowledge Object replication (Option C), and the Cluster Manager is still required (Option D) to coordinate cluster activities.

References (Splunk Enterprise Documentation):

• SmartStore Overview and Architecture Guide

• SmartStore Deployment and Configuration Manual

• Managing Storage and Compute Independence in Indexer Clusters

• Splunk Enterprise Capacity Planning – SmartStore Sizing Guidelines

All of the options are possible causes of a crash in Splunk. According to the Splunk documentation1, incorrect ulimit settings can lead to file descriptor exhaustion, which can cause Splunk to crash or hang. Insufficient disk IOPS can also cause Splunk to crash or become unresponsive, as Splunk relies heavily on disk performance2. Insufficient memory can cause Splunk to run out of memory and crash, especially when running complex searches or handling large volumes of data3. Running out of disk space can cause Splunk to stop indexing data and crash, as Splunk needs enough disk space to store its data and logs4.

1: Configure ulimit settings for Splunk Enterprise 2: Troubleshoot Splunk performance issues 3: Troubleshoot memory usage 4: Troubleshoot disk space issues

According to the Splunk documentation1, when migrating an indexer cluster from single-site to multi-site, you must remove the existing single-site attributes from the server.conf file of each peer node. These attributes include replication_factor, search_factor, and cluster_label. You must also restart each peer node after removing the attributes. The other options are false because:

Multi-site policies will apply only to the data created after migration, unless you configure the manager node to convert legacy buckets to multi-site1.

All peer nodes do not need to run the same version of Splunk, as long as they are compatible with the manager node2.

Single-site buckets can be converted to multi-site buckets by changing the constrain_singlesite_buckets setting in the manager node’s server.conf file to "false"1.

The repFactor (replication factor) attribute in the indexes.conf file determines whether an index participates in indexer clustering and how many copies of its data are replicated across peer nodes.

When repFactor is set to 0, it explicitly instructs Splunk to exclude that index from participating in the cluster replication and management process. This means:

The index is not replicated across peer nodes.

It will not be managed by the Cluster Manager.

It exists only locally on the indexer where it was created.

Such indexes are typically used for local-only storage, such as _internal, _audit, or other custom indexes that store diagnostic or node-specific data.

By contrast:

repFactor=auto allows the index to inherit the cluster-wide replication policy from the Cluster Manager.

available_sites and site_mappings relate to multisite configurations, controlling where copies of the data are stored, but they do not remove the index from clustering.

Setting repFactor=0 is the only officially supported way to create a non-clustered index within a clustered environment.

References (Splunk Enterprise Documentation):

• indexes.conf Reference – repFactor Attribute Explanation

• Managing Non-Clustered Indexes in Clustered Deployments

• Indexer Clustering: Index Participation and Replication Policies

• Splunk Enterprise Admin Manual – Local-Only and Clustered Index Configurations

 The logical first step when starting a deployment plan is to collect the initial requirements for the deployment from all stakeholders. This includes identifying the business objectives, the data sources, the use cases, the security and compliance needs, the scalability and availability expectations, and the budget and timeline constraints. Collecting the initial requirements helps to define the scope and the goals of the deployment, and to align the expectations of all the parties involved.

Inventorying the currently deployed logging infrastructure, determining what apps and use cases will be implemented, and gathering statistics on the expected adoption of Splunk for sizing are all important steps in the deployment planning process, but they are not the logical first step. These steps can be done after collecting the initial requirements, as they depend on the information gathered from the stakeholders.