SPLK-2002 Performance Tuning Practice Test 2 — 15 Questions

Question 1

You are analyzing search performance using the Search Activity dashboard. You observe that 'Result Count' is extremely high for a specific saved search. Which optimization technique is most effective to reduce the load on the indexer?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use specific time ranges and selective filters at the beginning of the search. To reduce the load on the indexer when a saved search returns an extremely high result count, the most effective optimization is to use specific time ranges and selective filters at the beginning of the search. This reduces the amount of data that the indexer must scan and process, directly lowering the load. Other options either do not address indexer load or are incorrect.

Answer

Increase the memory allocation for the search head

Answer

Disable data model acceleration for the index

Answer

Use the | table command at the start

Question 2

A user complains that a search is slow despite having a small time range. The Search Inspector reveals 'event_count' is high, but 'scanned_count' is also high. What is the likely cause?

Accepted Answer

The search is not using an indexed field to filter data. A high 'scanned_count' alongside a high 'event_count' means Splunk is reading many events from disk even though the time range is small — the search is not narrowing the dataset with an indexed field (like index, sourcetype, host, or source) before the base search runs. Without an indexed filter, Splunk must scan raw buckets and apply filtering at search time, which is far slower. Adding an indexed field to the base search dramatically reduces scanned_count.

Answer

The indexer has too much memory

Answer

Data model acceleration is failing

Answer

The search head is underpowered

Question 3

In a multi-site indexer cluster, which setting controls the number of copies of data kept per site?

Accepted Answer

site_replication_factor. In a multi-site indexer cluster, site_replication_factor is the setting in server.conf (under the [clustering] stanza) that defines how many copies of each bucket must exist within each site. It works alongside replication_factor, which sets the total number of copies across all sites. This ensures site-level redundancy so that a site failure does not cause data loss.

Answer

max_site_copies

Answer

site_data_count

Answer

cross_site_replication

Question 4

Which command helps you understand if your search is retrieving too much data from the disk by analyzing the 'index_time' and 'search_time'?

Accepted Answer

Search Inspector. The Search Inspector in Splunk is a built-in tool that provides detailed performance metrics for a specific search job, including the time spent on index_time (disk I/O) and search_time (processing). By examining these timings, you can determine if the search is reading excessive data from disk, which often indicates inefficiencies like lack of filtering or improper use of indexes. It is specifically designed for this diagnostic purpose, making it the correct choice.

Answer

Monitoring Console Search Activity Dashboard

Answer

btool

Answer

splunkd log analysis

Question 5

When tuning search performance, what is the 'join' command's primary drawback in terms of resource usage?

Accepted Answer

It consumes significant memory on the search head. The join command in Splunk loads the entire subsearch result set into memory on the search head and holds it while matching against the main search results. This in-memory hash/merge operation is memory-intensive and can trigger search head memory pressure or the subsearch result limit. That memory cost on the search head is the command's primary resource drawback.

Answer

It requires data to be fully ingested before searching

Answer

It causes high disk I/O on the indexer

Answer

It disables parallel processing

Question 6

What is the primary benefit of 'bucket rolling' in an indexer?

Accepted Answer

Allows the indexer to optimize disk I/O by isolating active writing to a single bucket. Bucket rolling closes the currently active (hot) bucket once it reaches a size or age threshold and opens a new hot bucket for incoming data. This isolates all active writes to a single bucket, so the indexer avoids random writes across many files and can optimize disk I/O and caching. Older buckets become warm/cold and are read-only, which further improves write efficiency.

Answer

Enables the indexer to bypass the parsing queue

Answer

Increases the size of the bucket for better compression

Answer

Reduces the requirement for indexes to be searchable

Question 7

What is the impact of placing a very high number of indexes on a single indexer?

Accepted Answer

Increased file handle usage and potential performance degradation. Placing a very high number of indexes on a single indexer increases the number of open file handles because each index maintains multiple bucket files (rawdata, tsidx, etc.). This can lead to file handle exhaustion, increased memory usage for bucket metadata, and overall performance degradation, especially during search and indexing.

Answer

Reduced licensing costs

Answer

Improved search speed

Answer

Faster data ingestion

Question 8

Which tool is best for monitoring the health and performance of the entire Splunk environment?

Accepted Answer

Monitoring Console. The Monitoring Console (formerly known as the Distributed Management Console) is Splunk's built-in tool specifically designed to monitor the health, performance, and topology of an entire Splunk deployment. It provides dashboards for indexing, search, resource usage, and license usage across all components. It is the recommended tool for holistic environment monitoring.

Answer

Splunk CLI

Answer

Deployment Server

Answer

Splunk Web UI

Question 9

Which Splunk process is responsible for managing the indexer's disk I/O and bucket lifecycle?

Accepted Answer

splunkd. splunkd is the main Splunk daemon process that runs on all Splunk instances, including indexers, and is responsible for managing indexing, search, and various background tasks such as disk I/O and bucket lifecycle management. It handles the rolling of buckets from hot to warm to cold to frozen, as well as disk space monitoring.

Answer

mongod

Answer

python

Answer

kvstore

Question 10

To optimize search performance, which feature should be enabled to allow Splunk to pre-calculate results for specific reports?

Accepted Answer

Search acceleration. Search acceleration (also called report acceleration) uses precomputed summaries stored in the index to speed up searches that match a saved report's base search. When enabled on a saved report, Splunk builds and maintains summary data so subsequent runs of that report or similar searches return results much faster. This is the feature specifically designed to pre-calculate results for reports.

Answer

Summary indexing

Answer

Parallel ingestion

Answer

Indexer clustering

Question 11

Which configuration file is used to specify the disk path where index buckets are stored?

Accepted Answer

indexes.conf. The indexes.conf file is the central configuration file for defining and managing indexes in Splunk. Within each index stanza, the 'homePath' setting specifies the directory where the index's hot and warm buckets are stored, and 'coldPath' specifies where cold buckets are stored. Therefore, indexes.conf is the correct file to specify the disk path for index buckets.

Answer

server.conf

Answer

outputs.conf

Answer

inputs.conf

Question 12

If a search head is overloaded with concurrent searches, what is the best strategy to offload the processing?

Accepted Answer

Implement a Search Head Cluster. A Search Head Cluster distributes search workload across multiple search head members, allowing concurrent searches to be load-balanced and providing horizontal scaling for search processing. This directly addresses an overloaded single search head by adding capacity and redundancy. Increasing indexers or indexer memory addresses indexing/search execution at the indexer layer, not the search head's concurrency bottleneck.

Answer

Increase the number of indexers

Answer

Reduce the number of users

Answer

Increase indexer memory

Question 13

What is the impact of having too many small buckets in an index?

Accepted Answer

Degraded search performance due to high overhead. Having too many small buckets in an index degrades search performance because each bucket must be opened and processed separately, leading to high overhead in terms of file handles, memory, and search initialization. Splunk searches across many small buckets require more resources and time compared to fewer, larger buckets. This is a common issue when buckets are rolled frequently due to small size limits.

Answer

Faster data rolling

Answer

Reduced disk space usage

Answer

Improved search performance

Question 14

Which TWO actions can improve the performance of a Splunk search head?

Accepted Answer

Adding more nodes to a search head cluster. Option A is correct because adding more nodes to a search head cluster distributes the search workload across multiple search heads, allowing concurrent searches to be parallelized and reducing the load on any single node, which directly improves search performance and scalability. Option E is correct because increasing system memory on the search head allows more search artifacts, result sets, and in-memory operations (such as those used by the search scheduler and knowledge objects) to be cached, reducing disk I/O and speeding up search execution. Option B is not correct because reducing the number of forwarders decreases data ingestion, not search performance, and may actually reduce the data available for searches. Option C is not correct because increasing license volume only allows more data to be indexed per day; it does not improve search speed or search head performance. Option D is not correct because disabling the indexer cluster would remove data replication and distributed search capabilities, harming availability and performance rather than improving it.

Answer

Reducing the number of forwarders

Answer

Increasing the license volume

Answer

Disabling the indexer cluster

Question 15

Which THREE factors commonly cause high CPU utilization on indexers?

Accepted Answer

Excessive data model acceleration jobs. Option A is correct because data model acceleration runs scheduled searches that build and periodically rebuild summary datasets on the indexer, and these background jobs consume significant CPU cycles, especially with many accelerated models. Option B is correct because complex or inefficient search queries (heavy use of wildcards, large time ranges, expensive commands like transaction, join, or stats) force the indexer to process large volumes of events and drive CPU utilization up. Option E is correct because high ingestion volume increases parsing, indexing, and pipeline processing work on the indexer, which is CPU-intensive and scales directly with the amount of incoming data. Option C does not belong because network latency affects search dispatch and data transfer speeds, not indexer CPU consumption. Option D does not belong because low disk space causes indexing to pause or block and triggers disk-related warnings, but it does not itself raise CPU utilization.

Answer

Network latency between indexers

Answer

Low disk space