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| Section | Objectives |
|---|---|
| Topic 1: Indexer Clustering | - Failure recovery and resilience - Cluster master configuration - Replication and search factor management |
| Topic 2: Splunk Architecture Fundamentals | - Forwarder and indexer roles - Data flow and pipeline architecture - Distributed architecture concepts |
| Topic 3: Search Head Architecture | - Search head clustering - Knowledge object distribution - Search performance optimization |
| Topic 4: Security and Authentication | - Encryption and data protection - Role-based access control (RBAC) - Authentication mechanisms |
| Topic 5: Data Management and Indexing | - Data retention and lifecycle management - Index configuration and management - Parsing and indexing process |
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NEW QUESTION # 33
Which of the following will cause the greatest reduction in disk size requirements for a cluster of N indexers running Splunk Enterprise Security?
Answer: B
NEW QUESTION # 34
Splunk Enterprise platform instrumentation refers to data that the Splunk Enterprise deployment logs in the
_introspectionindex. Which of the following logs are included in this index? (Select all that apply.)
Answer: B,D
Explanation:
Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/7.3.1/Troubleshooting/ Abouttheplatforminstrumentationframework
NEW QUESTION # 35
What is the recommended order of activities in the Splunk deployment process?
Answer: B
Explanation:
Splunk's official planning guidance explains that the deployment cycle begins with defining the system architecture, capacity planning, platform design, and topologies. Splunk states that before any component is installed, administrators must complete "infrastructure planning and buildout," which includes determining indexer capacity, search head roles, clustering strategy, storage layout, and performance requirements. This foundational step ensures that all Splunk components have the proper hardware, network design, and scaling expectations.
After the environment is built, Splunk documentation states that the next stage is "deployment and data onboarding," which includes configuring indexers, forwarders, parsing rules, event processing pipelines, data source validation, and enrichment steps such as field extractions, tagging, event types, and CIM alignment.
Splunk describes this as the phase where you bring in data and confirm correctness, completeness, and normalization.
Only after the system is stable and populated with data does Splunk recommend "user planning and rollout", which includes developing dashboards, roles, knowledge objects, search best practices, and enabling user access. Splunk emphasizes that user onboarding should occur last, once infrastructure and data pipelines are fully validated.
References:Splunk Admin & Architect Study Guide; Splunk Deployment Planning Guidelines; Splunk Validated Architectures (Planning and Design Sections).
NEW QUESTION # 36
(A high-volume source and a low-volume source feed into the same index. Which of the following items best describe the impact of this design choice?)
Answer: B,C
Explanation:
The Splunk Managing Indexes and Storage Documentation explains that when multiple data sources with significantly different ingestion rates share a single index, index bucket management is governed by volume- based rotation, not by source or time. This means that high-volume data causes buckets to fill and roll more quickly, which in turn causes low-volume data to age out prematurely, even if it is relatively recent - hence Option C is correct.
Additionally, because Splunk organizes data within index buckets based on event time and storage characteristics, low-volume data mixed with high-volume data results in inefficient searches for smaller datasets. Queries that target the low-volume source will have to scan through the same large number of buckets containing the high-volume data, leading to slower-than-necessary search performance - Option B.
Compression efficiency (Option A) and performance optimization through data mixing (Option D) are not influenced by mixing volume patterns; these are determined by the event structure and compression algorithm, not source diversity. Splunk best practices recommend separating data sources into different indexes based on usage, volume, and retention requirements to optimize both performance and lifecycle management.
References (Splunk Enterprise Documentation):
* Managing Indexes and Storage - How Splunk Manages Buckets and Data Aging
* Splunk Indexing Performance and Data Organization Best Practices
* Splunk Enterprise Architecture and Data Lifecycle Management
* Best Practices for Data Volume Segregation and Retention Policies
NEW QUESTION # 37
A customer plans to ingest 600 GB of data per day into Splunk. They will have six concurrent users, and they also want high data availability and high search performance. The customer is concerned about cost and wants to spend the minimum amount on the hardware for Splunk. How many indexers are recommended for this deployment?
Answer: A
Explanation:
Explanation
Two indexers clustered is the recommended deployment for a customer who plans to ingest 600 GB of data per day into Splunk, has six concurrent users, and wants high data availability and high search performance.
This deployment will provide enough indexing capacity and search concurrency for the customer's needs, while also ensuring data replication and searchability across the cluster. The customer can also save on the hardware cost by using only two indexers. Two indexers not in a cluster will not provide high data availability, as there is no data replication or failover. Three indexers not in a cluster will provide more indexing capacity and search concurrency, but also more hardware cost and no data availability. The customer's data retention period, number of long searches, or volume of saved/scheduled searches are not relevant for determining the number of indexers. For more information, see [Reference hardware] and [About indexer clusters and index replication] in the Splunk documentation.
NEW QUESTION # 38
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