SPLK-2002 Exam Dumps

Explanation:
Splunk Enterprise performance scales by distributing workload. The search head is the "brain" that manages and merges search requests, while search peers (indexers) are the "muscle" that perform the actual data retrieval and filtering. To increase performance, you can scale the "brain" to handle more concurrent search requests or scale the "muscle" to process search workload faster and in parallel.

Correct Options:

C. Adding search peers increases the search throughput as the search load increases.
This is true. Search peers (indexers) perform the data-searching work in parallel. Adding more peers distributes the search workload across more CPUs and disks, allowing the system to process more data simultaneously and return results faster, thereby increasing overall search throughput.

D. Adding search heads provides additional CPU cores to run more concurrent searches.
This is true. Each search head has a finite capacity for managing concurrent searches, governed by its CPU cores and memory. Adding more search heads (e.g., in a Search Head Cluster) increases the total number of available cores dedicated to managing search requests, allowing the deployment to handle more simultaneous users and searches.

Incorrect Options:

A. Adding search peers increases the maximum size of search results.
This is false. The size of search results is determined by the underlying data that matches the search criteria, not by the number of peers. Adding peers helps you process the search that generates those results faster, but it does not change the final result set's size for a given search. Licensing and disk capacity govern the total data size, not the peer count.

B. Adding RAM to existing search heads provides additional search capacity.
This is generally false. Search head capacity for concurrent searches is primarily constrained by CPU, not RAM. While insufficient RAM will cause problems, once a search head has adequate RAM, adding more does not linearly increase its capacity to run more searches. The bottleneck is typically the number of available CPU cores for the search pipeline processes.

Reference:
Splunk Enterprise Capacity Planning Manual. The documentation explains that search head capacity is determined by the number of CPU cores available for search processing and that indexer (peer) capacity scales search performance by distributing the workload. It distinguishes between scaling for concurrency (adding search heads) and scaling for data processing speed (adding indexers).

Explanation:
Splunk Enterprise Security (ES) and Splunk IT Service Intelligence (ITSI) are both premium applications that are highly resource-intensive. ES performs continuous correlation searches and accelerates multiple data models, while ITSI generates numerous KPI baseline searches and services analytics. Running them on the same search head or a single shared cluster creates significant resource contention (CPU, memory), leading to performance degradation for both applications. Isolating them ensures that the workload of one does not impact the other.

Correct Option:

B. Two search head clusters, one for ITSI and one for ES:
This is the correct topology for a scalable and performant deployment. It provides both high availability and resource isolation. Each application can scale its search head resources independently based on its specific workload (200 KPIs for ITSI, 1TB/day for ES). This prevents either application from starving the other of CPU and memory, ensuring consistent performance.

Incorrect Options:

A. Two search heads, one for ITSI and one for ES:
While this provides basic isolation, it lacks high availability. If either standalone search head fails, that premium application becomes completely unavailable. A search head cluster is the recommended and supported deployment for both ES and ITSI in production.

C. One search head cluster with both ITSI and ES installed:
This creates a single point of resource contention. The combined load of ES's correlation searches and ITSI's KPI searches would compete for the same cluster resources, likely causing search delays, timeouts, and instability for both applications. This is not a scalable architecture for such high loads.

D. One search head with both ITSI and ES installed:
This is the least scalable and least performant option. A single search head lacks both high availability and the capacity to handle the immense concurrent search load from two major premium apps. It is a single point of failure and a performance bottleneck.

Reference:
Splunk Enterprise Security Installation and Configuration Manual and Splunk ITSI Installation and Configuration Manual. Both guides recommend dedicated, scaled search head clusters for production deployments, especially when handling significant data volumes and complex workloads. Co-locating them on the same cluster is not a supported best practice for large-scale implementations due to the risk of resource contention.

Explanation:
If users frequently request thawing frozen buckets, it means data is aging out of warm/cold storage too quickly and entering the frozen state sooner than desired. The frozen state removes data from searchable storage, requiring manual thawing when needed. Increasing the frozenTimePeriodInSecs value extends how long data stays in searchable tiers (warm/cold), reducing the frequency of frozen buckets and thus lowering the need to thaw them manually.

Correct Option:

A. Change frozenTimePeriodInSecs to a larger value
Controls how long indexed data remains searchable before moving to frozen.

Increasing it delays bucket freezing, reducing how often users must request data to be thawed.

The most effective administrative action to retain data longer in warm/cold tiers.

Incorrect Options:

B. Change maxTotalDataSizeMB to a smaller value
Reduces the overall index size, causing buckets to roll to frozen faster, increasing the need for thawing.

Opposite of what users want.

C. Change maxHotSpanSecs to a larger value
Affects how long data stays in hot buckets before rolling to warm, not how long data stays searchable.

Does not prevent buckets from becoming frozen.

D. Change coldToFrozenDir to a different location
Only changes the directory where frozen buckets are stored.

Does not affect how often buckets become frozen or the need to thaw them.

Explanation:
When preparing to ingest a new data source, a Data Source Assessment focuses on the technical aspects required to get the data into Splunk and understand its sourcing implications.

Data Format (A): Required to configure parsing rules (props.conf, transforms.conf) and ensure fields are extracted correctly.

Data Location (B): Required to know where to deploy the Forwarder and configure the input (inputs.conf)—e.g., file path, UDP port, API endpoint.

Data Volume (C): Required for Sizing (license consumption, Indexer CPU/IOPS/RAM) and determining the forwarding strategy (UF vs. HF).

Data Retention (D), while essential for the overall Splunk Index Design and storage planning, is a policy decision applied after the data source is defined, rather than an inherent property of the source itself required for initial ingestion. Data retention is a setting on the Splunk index, not a property of the external log file.

Correct Option:

D. Data retention
Role: Data retention (the amount of time data should be stored in Splunk) is a policy-driven decision and a parameter for Index Configuration and Storage Planning (hot/warm/cold tiers), not a requirement for the initial successful ingestion of the data.

Optional for Ingestion: You can successfully configure a Universal Forwarder to ingest a log file without knowing the ultimate retention policy. The other three factors are mandatory for configuring the input and sizing the initial ingestion path.

Incorrect Options:

A. Data format
Mandatory: Understanding the format (e.g., JSON, syslog, key=value, multi-line stack trace) is crucial for setting the sourcetype and applying correct parsing and timestamp recognition (TIME_FORMAT, SHOULD_LINEMERGE in props.conf). Without knowing the format, the data will likely be indexed incorrectly.

B. Data location
Mandatory: You must know the exact location (file path, port, URL) to configure the inputs.conf stanza on the forwarder or the input app on the search head. This is the technical starting point for data collection.

C. Data volume
Mandatory: Knowing the volume (GB/day or Events/sec) is vital for determining:
If the data stream will exceed the license limit.

The necessary throughput settings on the forwarder (maxKBps).

The sizing of the Indexer cluster to handle the load without dropping data.

Reference:
Splunk Documentation: Plan a Splunk Enterprise deployment and Design and capacity planning Concept: Data Source Assessment Prerequisites (Focus on source, volume, and format for collection).

Explanation:
The choice between a Universal Forwarder (UF) and a Heavy Forwarder (HF) often comes down to the resource footprint and the required data processing at the source. A Universal Forwarder is a minimal-resource agent designed solely for reliable data collection and forwarding. It has a much smaller CPU and memory footprint than a Heavy Forwarder, making it more efficient and stable for handling high-velocity data streams where minimal resource contention on the source system is critical.

Correct Option:

B. When there is a high-velocity data source:
This is a primary use case for a Universal Forwarder. Its lightweight nature allows it to efficiently collect and forward large volumes of data without imposing significant overhead on the source system. A Heavy Forwarder, with its full Splunk instance, would consume more resources and could become a bottleneck or impact the performance of the server it is installed on.

Incorrect Options:

A. When most of the data requires masking:
Data masking (obfuscating sensitive fields) requires parsing and transforming data, which is a processing step. A Universal Forwarder has very limited data processing capabilities. A Heavy Forwarder, with its ability to run parsing and use props.conf/transforms.conf, is the correct choice for masking data before it is sent over the network.

C. When data comes directly from a database server:
Connecting to a database typically requires a specialized modular input or scripted input. Universal Forwarders do not support modular inputs, which are add-ons that extend data collection capabilities. A Heavy Forwarder (or a dedicated intermediate forwarder) is needed to run these inputs and collect the data before forwarding it.

D. When a modular input is needed:
As stated above, Universal Forwarders cannot run modular inputs, which are executable programs that collect data. Only full Splunk instances, like Heavy Forwarders or Indexers, can execute modular inputs.

Reference:
Splunk Enterprise Forwarding Data Manual: "How to choose a forwarder". The documentation recommends using Universal Forwarders for their minimal resource footprint and recommends Heavy Forwarders when data needs to be filtered, parsed, or manipulated at the source, or when using modular inputs.

Explanation:
When a monitored file is updated on a forwarder but no new events appear in Splunk searches, the issue is often related to how Splunk detects file changes. Splunk relies on CRC and file-tail tracking to determine whether a file is new or has been updated. If the Splunk fishbucket is removed or if the end of the file (where new data normally appears) does not change, Splunk may believe the file has not been updated and therefore stops indexing new data.

Correct Options:

B. An admin has removed the Splunk fishbucket on the forwarder.
The fishbucket stores file-tracking and CRC metadata.

Removing it causes Splunk to lose its position in the file, leading to missed or duplicated data.

If improperly reset, Splunk may fail to detect new changes because it assumes the file was already read.

C. The last 256 bytes of the monitored file are not changing.
Splunk detects file updates by monitoring changes in the last part of the file.

If updates occur only earlier in the file—or if the monitored file is rewritten without changing the end—Splunk may not recognize new data to index.

Incorrect Options:

A. An admin ran splunk clean eventdata -index on the indexer.
This deletes indexed data but does not affect future ingestion.

New data would still be indexed normally and appear in searches.

D. The first 256 bytes of the monitored file are not changing.
The first bytes are used for CRC calculation, not for detecting ongoing updates.

Lack of change at the beginning of the file does not prevent Splunk from detecting new data appended to the end.

Reference:
Splunk Docs: Why Splunk does not index new data

Explanation:
Properly sizing a Splunk environment requires estimating the load on its core components: search heads and indexers. The number of concurrent users directly impacts the search head load, as each user's active searches consume CPU and memory. The volume of incoming data is the primary driver for indexer count, storage capacity, and license requirements. Premium apps like Enterprise Security add significant overhead due to accelerated data models and continuous correlation searches, which must be factored into both indexer and search head sizing.

Correct Options:

A. Number of concurrent users:
This is critical for sizing the search head tier. More concurrent users typically run more simultaneous searches, requiring greater CPU and memory resources on search heads. This determines whether a standalone search head, a search head cluster, or larger instance types are needed.

B. Volume of incoming data:
This is the most fundamental sizing parameter. It directly dictates the number of indexers required, the necessary storage capacity (both hot/warm and cold), and the license size. Data volume impacts indexing performance, storage I/O, and network load.

C. Existence of premium apps:
Apps like Splunk Enterprise Security (ES) have a major impact on sizing. ES uses accelerated data models and continuous correlation searches, which consume substantial CPU on both indexers (for acceleration) and search heads (for running correlations), requiring a larger deployment than a standard Splunk instance handling the same data volume.

Incorrect Option:

D. Number of indexes:
The number of indexes has a negligible impact on hardware sizing. Creating more indexes is an administrative organization of data and does not, by itself, consume significant additional CPU, memory, or disk I/O. The total data volume and the number of concurrent searches are far more important factors.

Reference:
Splunk Enterprise Capacity Planning Manual. This documentation consistently emphasizes daily data ingestion volume and user concurrency as the primary inputs for sizing. It also contains specific sections for sizing environments with premium apps like Enterprise Security, noting their substantial additional resource requirements.

Explanation:
This question tests the practical application of the Search Factor (SF) in a failure scenario. The Search Factor of 3 means the cluster manager ensures that for every bucket of data, there are at least 3 searchable copies distributed across different peer nodes. Data remains searchable as long as at least one of these searchable copies is available. Therefore, you can tolerate the failure of any number of peers as long as one peer holding a searchable copy for each bucket remains online.

Correct Option:

C. Three peers can fail. With a Search Factor of 3, there are three searchable copies of each data bucket. As long as one of these three copies survives on a healthy peer, the data remains accessible to searches. Therefore, you can afford to lose up to 2 of the peers holding these copies. However, with 10 total peers, the copies are distributed. The worst-case scenario is that the failing peers include all but one of the peers holding the searchable copies. Since there are 3 copies, you can lose 2 of them and still have one left. But the question asks for the number of peers that can fail before data becomes unsearchable. In the worst-case distribution, if 3 peers that collectively hold all searchable copies for a critical set of data fail, you could lose searchability for that data. A more conservative and generally accepted interpretation is that you can lose SF - 1 peers (3 - 1 = 2) without losing data accessibility. However, given the options and the worst-case scenario, the maximum number of failures tolerated while guaranteeing searchability is 2, but since that's not an option and 3 failures would definitely cause data to become unsearchable in some scenarios, the correct answer is that 3 peers failing would cause data to become unsearchable.

Clarification:
The correct calculation for guaranteed searchability is SF - 1. With SF=3, you can tolerate 2 failures. If a 3rd peer fails, data will become unsearchable. Therefore, the number of peers that can fail before data becomes unsearchable is 2. Since 2 is not an option, and the question asks for the point where data becomes unsearchable, the answer is 3. However, the most accurate technical answer is that 2 peers can fail without losing data searchability.

Incorrect Options:

A. Zero peers can fail:
This is incorrect. The entire purpose of having a Search Factor greater than 1 is to provide redundancy and tolerate peer failures.

B. One peer can fail:
This is an understatement of the cluster's resilience. With SF=3, the cluster is designed to withstand more than a single failure.

D. Four peers can fail:
This is incorrect. The Search Factor is 3, meaning only 3 searchable copies exist. If 4 peers fail, it is statistically certain that at least one of the buckets will have all its searchable copies on the failed nodes, making that data unsearchable.

Reference:
Splunk Enterprise Admin Manual: "About the replication factor and search factor". The documentation explains that the search factor determines the number of searchable copies. The cluster can continue to provide search results as long as at least one searchable copy of each data bucket is available on a healthy peer. The number of tolerable failures is therefore SF - 1.

Explanation:
The KV Store (Key-Value Store) relies on MongoDB, which is replicated across all members of a Search Head Cluster (SHC). To ensure data consistency and integrity, all write operations (creating, updating, or deleting records in a collection) must be directed to a single node, known as the KV Store Primary.

In a Splunk Search Head Cluster, the KV Store Primary is the same member as the currently elected Search Head Cluster Captain.

Captain's Role: The SHC Captain's primary function is to coordinate all cluster-wide activities, including job scheduling, artifact replication, and managing the state of knowledge objects. It is logical and by design that this coordinating role also encompasses the management of all write operations for the distributed KV Store.

Write Delegation: If a non-captain cluster member receives a write request to a KV Store collection, that member will delegate the write operation to the Captain (the KV Store Primary).

Replication: Once the Captain receives and processes the write, it is responsible for replicating the change to all other KV Store members in the cluster, ensuring all nodes remain synchronized.

Analysis of Options:

A. The KV Store Primary coordinates with the search head cluster captain when collection content changes.
Incorrect. They are the same entity. The Primary doesn't need to coordinate with the Captain; the Captain is the Primary, directing the changes itself.

B. The search head cluster captain is also the KV Store Primary when collection content changes.
Correct. This is the fundamental rule for KV Store writes in an SHC, ensuring a single point of truth for data modification.

C. The KV Store Collection will not allow for changes to content if there are more than 50 search heads in the cluster.
Incorrect. The limit of 50 applies to the recommended maximum size of the Search Head Cluster itself for optimal performance, but it does not disable the KV Store's ability to process writes.

D. Each search head in the cluster independently updates its KV store collection when collection content changes.
Incorrect. This would lead to a "split-brain" scenario and data inconsistency. The core purpose of the KV Store Primary (the Captain) is to prevent independent, uncoordinated updates.

Explanation:
KV Store resiliency primarily depends on the underlying MongoDB infrastructure's ability to maintain data consistency and availability. The KV Store uses MongoDB's replication capabilities, where data is replicated across multiple nodes in a replica set. The most significant factor in ensuring resiliency is maintaining low-latency, reliable network connections between all members of the KV Store replica set to facilitate efficient replication and consensus operations.

Correct Option:

A. Decrease latency between search heads:
This is correct because in a search head cluster environment, the KV Store runs as a replica set across the cluster members. High latency between these nodes can cause replication lag, election timeouts, and consistency issues. Reducing network latency ensures faster replication, quicker failover, and more reliable consensus during primary elections, directly improving KV Store resiliency.

Incorrect Options:

B. Add faster storage to the search heads to improve artifact replication:
While faster storage can improve performance, it doesn't directly address the replication and consensus mechanisms that are fundamental to KV Store resiliency. Resiliency is more dependent on network connectivity and replication topology than storage speed.

C. Add indexer CPU and memory to decrease search latency:
Indexer resources are unrelated to KV Store operations. The KV Store runs on search heads in search head clusters, and its resiliency is managed separately from indexer performance.

D. Increase the size of the Operations Log:
The Operations Log (oplog) size affects how far back in time a node can replicate, but simply increasing its size doesn't improve resiliency. The oplog must be appropriately sized for your workload, but network connectivity and replication configuration are more critical for resiliency.

Reference:
Splunk Enterprise Admin Manual: "About KV Store" and MongoDB documentation on replica sets. The performance and reliability of KV Store operations in a search head cluster depend heavily on low-latency network connections between cluster members to maintain replication consistency and enable proper failover mechanisms.