Create and Maintain Performant Data Indexing. CYBERSECURITY-DEFENSE-ENGINEER Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   A company is deploying a Splunk indexer cluster to handle a growing volume of security data. They need to ensure high availability and scalability while maintaining indexing performance. Which configuration is essential for the cluster to function correctly?

   • AConfigure a cluster master to manage the indexers and replicate data across peers.
   • BSet up a load balancer in front of the indexers to distribute incoming data.
   • CUse a single large indexer to avoid replication overhead.
   • DEnable search head clustering to manage indexer replication.
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   A Splunk environment is ingesting data from multiple sources, and the indexer is struggling to keep up with the volume. The administrator wants to improve indexing throughput without adding more hardware. Which tuning technique is most effective?

   • AIncrease the number of indexing pipelines to allow parallel processing.
   • BDisable timestamp extraction to speed up parsing.
   • CReduce the number of inputs to lower the load.
   • DSet the indexer to use a single pipeline to reduce overhead.
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4. 18expert · hard

   A Splunk indexer cluster is experiencing degraded indexing performance. The cluster has replication factor 3 and search factor 2. The administrator notices that the cluster master is reporting 'peer down' for one indexer. What is the most likely impact on indexing performance?

   • AIndexing performance will stop entirely until the peer is back online.
   • BIndexing performance may degrade because the remaining peers must handle additional replication and search load.
   • CIndexing performance will improve because there is less replication overhead.
   • DIndexing performance will be unaffected because the other peers can handle the load.
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5. 19foundation · easy

   What is a common technique to improve indexing throughput when dealing with high-volume data?

   • AIncreasing the number of parallel ingestion pipelines
   • BDisabling indexer clustering
   • CDecreasing the number of indexers
   • DReducing the retention period
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6. 20expert · hard

   A Splunk environment is ingesting a high volume of data from multiple sources. The indexer is experiencing high CPU usage during parsing, and the administrator wants to reduce CPU load without losing data. Which approach is most effective?

   • AMove parsing to a heavy forwarder to offload CPU from the indexer.
   • BReduce the number of indexing pipelines to lower CPU usage.
   • CIncrease the parsing queue size to buffer more events.
   • DDisable timestamp extraction to speed up parsing.
   Reveal Solution Discussion Select an answer first