SPLK-3003 Exam Dumps

Explanation: This configuration item determines whether Splunk software merges multiple lines into single events. By default, it is set to true, which means that Splunk software attempts to merge lines that do not start with a timestamp into the previous line that has a timestamp. This can improve the accuracy of event breaking, but it can also degrade the performance of data ingestion, as it requires more processing and memory resources.
Setting SHOULD_LINEMERGE to false can significantly improve data ingestion performance, especially for data sources that have consistent event boundaries and do not need line merging. However, this can also result in incorrect event breaking for some data sources that have multi-line events or variable formats.
The other options are incorrect because they do not have a significant impact on data ingestion performance. Option A is incorrect because AUTO_KV_JSON is a configuration item that enables automatic key-value extraction for JSON data. It does not affect data ingestion performance, as it only applies to search-time processing. Option B is incorrect because BREAK_ONLY_BEFORE_DATE is a configuration item that controls how Splunk software breaks events based on timestamps. It does not affect data ingestion performance, as it only applies to search-time processing. Option D is incorrect because ANNOTATE_PUNCT is a configuration item that adds punctuation metadata to events for faster field extraction. It does not affect data ingestion performance, as it only applies to search-time processing.

Explanation: In a large cloud customer environment with many (>100) dynamically created endpoint systems, each with a UF already deployed, the best approach for associating these systems with an appropriate serverclass on the deployment server is to work with the cloud orchestration team to dynamically insert an appropriate clientName setting into each endpoint’s local/deploymentclient.conf which can be matched by whitelist in serverclass.conf. This approach allows the deployment server to easily identify and group the endpoints based on their clientName values, which can be customized according to the customer’s needs. For example, the clientName can be set to include information such as the endpoint’s role, function, location, or owner. The whitelist attribute in serverclass.conf can then use a simple pattern or regular expression to match the clientName values and assign the endpoints to the corresponding serverclass.

Explanation: This is because the Splunk App for AWS is a prebuilt app that is installed on the deployer and pushed to the search head cluster members. When a power user modifies a dashboard in the app on one of the search head cluster members, the changes are stored in the local directory of that member. When the app is upgraded to the latest version by following the instructions via the deployer, the changes in the default directory of the app are overwritten, but the changes in the local directory are preserved. Therefore, the power user will still see their modified version of the dashboard, while other users will see the updated version of the dashboard.
The other options are incorrect because they do not reflect what happens when a prebuilt app is upgraded in a search head cluster. Option A is incorrect because the updated dashboard will be deployed globally to all users, except for the power user who modified it. Option B is incorrect because applying the search head cluster bundle will not fail due to the conflict, as the local changes are not pushed back to the deployer. Option C is incorrect because the updated dashboard will not be available to the power user, as their local changes will take precedence over the default changes.

Explanation: When adding a new search head to a search head cluster (SHC), the following scenario occurs:
The new search head connects to the deployer and pulls the most recently deployed bundle. The deployer is a Splunk instance that manages the app configuration bundle for the SHC. The bundle contains the app configurations and knowledge objects that are common to all the search heads in the cluster. The new search head downloads and extracts the bundle to its etc/shcluster/apps directory.
The new search head connects to the captain and replays any recent configuration changes to bring it up to date. The captain is one of the search heads in the cluster that coordinates the cluster activities and maintains the cluster state. The captain keeps track of any configuration changes that are made on any of the cluster members, such as creating or modifying dashboards, reports, alerts, or macros. The new search head requests these changes from the captain and applies them to its own configuration.
By following these steps, the new search head synchronizes its configuration with the rest of the cluster and becomes a fully functional member.

Explanation: The Splunk Validated Architectures (SVAs) are proven reference architectures for stable, efficient and repeatable Splunk deployments. They offer topology options that consider a wide array of organizational requirements, so the customer can easily understand and find a topology that is right for their needs. The SVAs also provide design principles and best practices to help the customer build an environment that is easier to maintain and troubleshoot. The SVAs are available on the Splunk website and can be customized using the Interactive Splunk Validated Architecture (iSVA) tool. The other options are incorrect because they do not provide the customer with a reliable and tailored resource to help them design their new architecture. Option A is too vague and does not point the customer to a specific document or section. Option B is irrelevant and does not address the customer’s architectural needs. Option C is unreliable and does not guarantee that the customer will find a suitable solution for their requirements.

Explanation: The difference in the Splunk2Splunk payload traffic sent between a universal forwarder (UF) and indexer compared to the payload sent between a heavy forwarder (HF) and the indexer layer is that the UF sends a stream of data containing one set of metadata fields to represent the entire stream, whereas the HF sends individual events, each with their own metadata fields attached, resulting in a larger payload. This is because the UF does not parse or index the data before forwarding it, but rather sends it as raw data in 64K TCP chunks. The metadata fields, such as host, source, sourcetype, etc., are applied to the entire stream based on the inputs.conf configuration. The HF, on the other hand, parses and indexes the data before forwarding it, which means that it breaks the data into individual events and assigns metadata fields to each event based on props.conf and transforms.conf configuration. This results in a larger payload size, but also allows for more granular control over event processing and routing.