Pass the Splunk Enterprise Certified Architect SPLK-2002 Questions and answers with Dumpstech

Of the following types of files within an index bucket, the rawdata file type may consume the most disk. The rawdata file type contains the compressed and encrypted raw data that Splunk has ingested. The rawdata file type is usually the largest file type in a bucket, because it stores the original data without any filtering or extraction. The bloom filter file type contains a probabilistic data structure that is used to determine if a bucket contains events that match a given search. The bloom filter file type is usually very small, because it only stores a bit array of hashes. The metadata (.data) file type contains information about the bucket properties, such as the earliest and latest event timestamps, the number of events, and the size of the bucket. The metadata file type is also usually very small, because it only stores a few lines of text. The inverted index (.tsidx) file type contains the time-series index that maps the timestamps and event IDs of the raw data. The inverted index file type can vary in size depending on the number and frequency of events, but it is usually smaller than the rawdata file type

The frequency in which a deployment client contacts the deployment server is controlled by the phoneHomeIntervalInSecs attribute in deploymentclient.conf. This attribute specifies how often the deployment client checks in with the deployment server to get updates on the apps and configurations that it should receive. The polling_interval attribute in outputs.conf controls how often the forwarder sends data to the indexer or another forwarder. The polling_interval attribute in deploymentclient.conf and the phoneHomeIntervalInSecs attribute in outputs.conf are not valid Splunk attributes. For more information, see Configure deployment clients and Configure forwarders with outputs.conf in the Splunk documentation.

According to the Splunk documentation1, multi-site clustering is an indexer cluster that spans multiple physical sites, such as data centers. Each site has its own set of peer nodes and search heads. Each site also obeys site-specific replication and search factor rules. The use cases that are made possible by multi-site clustering are:

Greatly reduce WAN traffic by preferentially searching assigned site (search affinity). This means that if you configure each site so that it has both a search head and a full set of searchable data, the search head on each site will limit its searches to local peer nodes. This eliminates any need, under normal conditions, for search heads to access data on other sites, greatly reducing network traffic between sites2.

Seamlessly route searches to a redundant site in case of a site failure. This means that by storing copies of your data at multiple locations, you maintain access to the data if a disaster strikes at one location. Multisite clusters provide site failover capability. If a site goes down, indexing and searching can continue on the remaining sites, without interruption or loss of data2.

The other options are false because:

Use blockchain technology to audit search activity from geographically dispersed data centers. This is not a use case of multi-site clustering, as Splunk does not use blockchain technology to audit search activity. Splunk uses its own internal logs and metrics to monitor and audit search activity3.

Enable a forwarder to send data to multiple indexers. This is not a use case of multi-site clustering, as forwarders can send data to multiple indexers regardless of whether they are in a single-site or multi-site cluster. This is a basic feature of forwarders that allows load balancing and high availability of data ingestion4.

The correct answer is B. Set the Replication Factor based on allowed indexer failure. This is a best practice for adding data resiliency to a single-site indexer cluster, as it ensures that there are enough copies of each bucket to survive the loss of one or more indexers without affecting the searchability of the data1. The Replication Factor is the number of copies of each bucket that the cluster maintains across the set of peer nodes2. The Replication Factor should be set according to the number of indexers that can fail without compromising the cluster’s ability to serve data1. For example, if the cluster can tolerate the loss of two indexers, the Replication Factor should be set to three1.

The other options are not best practices for adding data resiliency. Option A, setting the Replication Factor to 49, is not recommended, as it would create too many copies of each bucket and consume excessive disk space and network bandwidth1. Option C, always using the default Replication Factor of 3, is not optimal, as it may not match the customer’s requirements and expectations for data availability and performance1. Option D, setting the Replication Factor based on allowed search head failure, is not relevant, as the Replication Factor does not affect the search head availability, but the searchability of the data on the indexers1. Therefore, option B is the correct answer, and options A, C, and D are incorrect.

1: Configure the replication factor 2: About indexer clusters and index replication

Indexed extraction configurations are processed in the indexing phase of the Splunk Enterprise data pipeline. The data pipeline is the process that Splunk uses to ingest, parse, index, and search data. Indexed extraction configurations are settings that determine how Splunk extracts fields from data at index time, rather than at search time. Indexed extraction can improve search performance, but it also increases the size of the index. Indexed extraction configurations are applied in the indexing phase, which is the phase where Splunk writes the data and the .tsidx files to the index. The input phase is the phase where Splunk receives data from various sources and formats. The parsing phase is the phase where Splunk breaks the data into events, timestamps, and hosts. The search phase is the phase where Splunk executes search commands and returns results.

According to Splunk Enterprise administrative documentation, the btool utility is used to troubleshoot configuration settings by merging and displaying effective configurations from all configuration files (system, app, and local levels). When diagnosing license master configuration issues on a search peer or any cluster node, Splunk recommends using the command that specifically lists the license-related stanzas from server.conf.

The correct command is:

splunk cmd btool server list license --debug

This command reveals all configuration parameters under the [license] stanza, including those related to license master connections such as master_uri, pass4SymmKey, and disabled flags. The --debug flag ensures full path tracing of each configuration file, making it easy to identify conflicting or missing parameters that cause communication or validation errors between a search peer and the license master.

Other commands listed, such as btool server list clustering, are meant for diagnosing cluster configuration issues (like replication or search factors), not licensing. The check --debug command only validates syntax and structure, not specific configuration errors tied to licensing. Therefore, the correct and Splunk-documented method for diagnosing license configuration problems on a search peer is to inspect the license stanza using the btool server list license --debug command.

References (Splunk Enterprise Documentation):

• btool Command Reference – Troubleshooting Configuration Issues

• server.conf – License Configuration Parameters

• Managing Distributed License Configurations in Search Peer Clusters

• Troubleshooting License Master and Peer Connectivity

Data Model acceleration is a feature that enables faster searches over large data sets by summarizing the raw data into a more efficient format. Data Model acceleration consumes additional disk space, as it stores both the raw data and the summarized data. The amount of disk space required depends on the size and complexity of the Data Model, the retention period of the summarized data, and the compression ratio of the data. According to the Splunk Enterprise Security Planning and Installation Manual, Data Model acceleration is one of the factors that strongly impacts storage sizing requirements for Enterprise Security. The other factors are the volume and type of data sources, the retention policy of the data, and the replication factor and search factor of the index cluster. The number of scheduled (correlation) searches, the number of Splunk users configured, and the number of source types used in the environment are not directly related to storage sizing requirements for Enterprise Security1

1: https://docs.splunk.com/Documentation/ES/6.6.0/Install/Plan#Storage_sizing_requirements

The correct tasks that the cluster manager performs in an indexer cluster are A. Generates and maintains the list of primary searchable buckets, B. If Indexer Discovery is enabled, provides the list of available peer nodes to forwarders, and D. Distributes app bundles to peer nodes. According to the Splunk documentation1, the cluster manager is responsible for these tasks, as well as managing the replication and search factors, coordinating the replication and search activities, and providing a web interface for monitoring and managing the cluster. Option C, ensuring all peer nodes are always using the same version of Splunk, is not a task of the cluster manager, but a requirement for the cluster to function properly2. Therefore, option C is incorrect, and options A, B, and D are correct.

1: About the cluster manager 2: Requirements and compatibility for indexer clusters

All of the options are possible causes of a crash in Splunk. According to the Splunk documentation1, incorrect ulimit settings can lead to file descriptor exhaustion, which can cause Splunk to crash or hang. Insufficient disk IOPS can also cause Splunk to crash or become unresponsive, as Splunk relies heavily on disk performance2. Insufficient memory can cause Splunk to run out of memory and crash, especially when running complex searches or handling large volumes of data3. Running out of disk space can cause Splunk to stop indexing data and crash, as Splunk needs enough disk space to store its data and logs4.

1: Configure ulimit settings for Splunk Enterprise 2: Troubleshoot Splunk performance issues 3: Troubleshoot memory usage 4: Troubleshoot disk space issues

A deployment plan should include business continuity and disaster recovery plans, current logging details and data source inventory, and current and future topology diagrams of the IT environment. These elements are essential for planning, designing, and implementing a Splunk deployment that meets the business and technical requirements. A comprehensive list of stakeholders, either direct or indirect, is not part of the deployment plan, but rather part of the project charter. For more information, see Deployment planning in the Splunk documentation.