The Splunk Professional Services (PS) preferred method for onboarding data in a production environment is to use configuration files, specifically inputs.conf. This method is highly scalable, repeatable, and allows for version control. In a distributed architecture, inputs.conf files are typically packaged within apps and managed centrally by a deployment server, ensuring consistent configuration across numerous forwarders. This approach provides the robustness and manageability required for enterprise-grade deployments, unlike UI-based methods which are better suited for testing or single-instance setups.

A. UDP is an unreliable, connectionless protocol. It does not guarantee data delivery, making it unsuitable for critical log collection where data loss is unacceptable.

B. The "add data" wizard in Splunk Web is primarily for ad-hoc data inputs, testing, or small-scale, single-instance deployments. It is not a scalable or manageable solution for enterprise environments.

D. A scripted input is a specialized tool for custom data sources (e.g., APIs, databases). Using it to monitor a standard log file is inefficient and overly complex; the monitor:// stanza is the correct and optimized method for that task.

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1. Splunk Enterprise Documentation, Getting Data In, "Configure inputs.conf": This section explicitly states, "The most common way to configure a data input is to add a stanza to an inputs.conf file." It details the process of creating and editing this file as the fundamental method for data onboarding.

2. Splunk Enterprise Documentation, Updating Splunk Enterprise Instances, "About deployment server and forwarder management": This document describes the best practice for managing configurations in a distributed environment. It states, "The deployment server is a tool for distributing configurations, apps, and content to groups of Splunk Enterprise instances... This is the primary way to manage a fleet of universal forwarders." This process relies entirely on managing configuration files like inputs.conf within deployment apps.

3. Splunk Enterprise Documentation, Forwarding Data, "Configure the universal forwarder": This guide outlines the steps for setting up forwarders, emphasizing the creation and deployment of a inputs.conf file to specify which data the forwarder should collect and send. This reinforces that file-based configuration is the standard operational procedure.

A Universal Forwarder (UF) is a lightweight agent designed for minimal resource usage. It reads raw data from a source and forwards it as an unparsed stream to the indexer. It attaches a single set of metadata (e.g., host, source, sourcetype) to the entire data stream or chunk. The indexer is then responsible for the parsing pipeline, which includes event breaking and timestamp extraction.

Conversely, a Heavy Forwarder (HF) is a full Splunk Enterprise instance. It performs the entire parsing pipeline locally before forwarding. It breaks the raw data into individual events, extracts timestamps, and can apply transforms. Consequently, it sends fully "cooked" individual events to the indexer, with each event carrying its own set of metadata. This per-event metadata overhead results in a larger total payload size compared to the UF's stream-based approach.

A: The payload format is fundamentally different (raw stream vs. parsed events), and the HF payload is larger due to per-event metadata, so they are not the same.

C: A Universal Forwarder never performs event breaking. This function is exclusively handled by the parsing pipeline on an indexer or a Heavy Forwarder.

D: This option incorrectly reverses the roles. The UF sends the raw stream with a single metadata set, while the HF sends individual, parsed events.

1. Splunk Enterprise Documentation, Forwarding Data Manual, "Types of forwarders": This section explicitly contrasts the forwarder types. It states, "A universal forwarder is a dedicated, streamlined version of Splunk Enterprise that contains only the essential components needed to forward data... It performs minimal processing on the data... It does not parse the data before forwarding it." For the heavy forwarder, it states, "A heavy forwarder is a full Splunk Enterprise instance... A heavy forwarder parses data before forwarding it." This confirms that the HF sends parsed, individual events while the UF sends an unparsed stream.

2. Splunk Enterprise Documentation, Getting Data In Manual, "The data pipeline": This document describes the stages of data processing. The "Parsing" phase, which breaks the data stream into individual events, occurs on the indexer for data received from a Universal Forwarder. For a Heavy Forwarder, this parsing occurs on the forwarder itself before the data is sent to the "Indexing" pipeline on the indexer. This architectural difference dictates the payload format described in the correct answer.

3. Splunk Enterprise Documentation, Forwarding Data Manual, "How forwarding works": This section details the underlying mechanism. Data from a Universal Forwarder is sent to the receivers/stream endpoint on an indexer, indicating a raw, unparsed stream. Data from a Heavy Forwarder is sent to the receivers/cooked endpoint, signifying that the data has already been processed (parsed) into individual events. This technical distinction directly supports the difference in payload structure.

The Monitoring Console (MC) functions as a dedicated search head to monitor the health and performance of a Splunk environment. To gather data, it must be configured to search all other Splunk instances. In a distributed deployment, this is achieved by adding the instances as search peers. For an indexer cluster, only the cluster master node is added as a search peer to the MC. The cluster master then manages and forwards search requests to the indexer peers. All other single-purpose instances, including standalone search heads, the deployment server, and the license master, must be added individually as search peers to the MC.

A. This is incorrect because it omits the search heads, deployment server, and license master, which are critical components that also require monitoring.

B. This is incorrect because individual indexers within a cluster are not added as search peers. The cluster master manages search coordination for the entire cluster.

D. This is incorrect as it fails to include the search heads and the entire indexer cluster (represented by the cluster master) for comprehensive monitoring.

1. Splunk Enterprise Documentation, Monitoring Splunk Enterprise, Version 9.2.1, "Set up the monitoring console in a distributed deployment". The section "Add instances as search peers" states: "Add all instances in your deployment as search peers to the monitoring console, except for the indexer peers in an indexer cluster. For an indexer cluster, you add the cluster manager node as a search peer." This directly supports adding the cluster master, search heads, deployment server, and license master, while excluding the individual indexers.

2. Splunk Enterprise Documentation, Distributed Search, Version 9.2.1, "How search heads interact with indexer clusters". This section clarifies the search relationship: "To enable a search head to search an indexer cluster, you must configure the cluster master as a search peer on the search head." The Monitoring Console acts as a search head in this context.

Putting an old indexer into manual detention is the correct procedure for planned maintenance, such as a hardware migration. When a peer node is in manual detention, the Cluster Manager is instructed not to initiate bucket-fixing activities for a configurable period (defaulting to 60 minutes). This directly prevents the cluster from starting unnecessary replication operations to create new copies of buckets from the peer being decommissioned. This allows the administrator to shut down the old indexer gracefully without causing a resource-intensive replication storm across the remaining peers in the cluster.

A. Disable the indexing ports on the old indexers.

This only stops new data from being indexed on the peer; it does not prevent the Cluster Manager from initiating bucket fixing for existing data if the peer goes offline.

B. Disable replication ports on the old indexers.

This would cause the Cluster Manager to see the peer as down or unresponsive, which would immediately trigger bucket-fixing activities, increasing replication operations.

D. Put the old indexers into automatic detention.

Automatic detention is a system-triggered state for unstable peers that restart too frequently. It is not a procedure initiated by an administrator for planned maintenance like a migration.

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1. Splunk Enterprise Documentation, Managing Indexers and Clusters of Indexers, "Take a peer offline".

This section explicitly details the procedure for planned maintenance. It states, "To take a peer offline for a short time, for maintenance or a configuration change, you can put it in manual detention... When you put a peer in manual detention, the cluster manager does not engage in bucket fixing for that peer's buckets for 60 minutes (by default)... This prevents the cluster from performing unnecessary bucket-fixing activities." This directly supports using manual detention (Option C) to reduce replication.

2. Splunk Enterprise Documentation, Admin Manual, server.conf specification file.

Under the [clustering] stanza, the documentation describes autodetentiononrestartcount. It specifies: "If the peer restarts more than times within the autodetentiononrestartperiod, the cluster manager automatically puts the peer into detention." This confirms that automatic detention (Option D) is a reactive mechanism for unstable peers, not a proactive method for planned migration.