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

 Search head clustering is a Splunk feature that allows a group of search heads to share configurations, apps, and knowledge objects, and to provide high availability and scalability for searching. Search head clustering has the following characteristics:

A deployer is required. A deployer is a Splunk instance that distributes the configurations and apps to the members of the search head cluster. The deployer is not a member of the cluster, but a separate instance that communicates with the cluster master.

At least three search heads are needed. A search head cluster must have at least three search heads to form a quorum and to ensure high availability. If the cluster has less than three search heads, it cannot function properly and will enter a degraded mode.

The deployer must have sufficient CPU and network resources to process service requests and push configurations. The deployer is responsible for handling the requests from the cluster master and the cluster members, and for pushing the configurations and apps to the cluster members. Therefore, the deployer must have enough CPU and network resources to perform these tasks efficiently and reliably.

Search heads do not need to meet the high-performance reference server requirements, as this is not a mandatory condition for search head clustering. The high-performance reference server requirements are only recommended for optimal performance and scalability of Splunk deployments, but they are not enforced by Splunk.

Increasing the number of parallel ingestion pipelines in server.conf is most likely to improve indexing performance when indexing is slow and real-time search results are delayed in a Splunk environment with two indexers and one search head. The parallel ingestion pipelines allow Splunk to process multiple data streams simultaneously, which increases the indexing throughput and reduces the indexing latency. Increasing the maximum number of hot buckets in indexes.conf will not improve indexing performance, but rather increase the disk space consumption and the bucket rolling time. Decreasing the maximum size of the search pipelines in limits.conf will not improve indexing performance, but rather reduce the search performance and the search concurrency. Decreasing the maximum concurrent scheduled searches in limits.conf will not improve indexing performance, but rather reduce the search capacity and the search availability. For more information, see Configure parallel ingestion pipelines in the Splunk documentation.

According to the Splunk documentation1, one of the benefits of search head clustering is the automatic replication of user knowledge objects, such as dashboards, reports, alerts, and tags. This ensures that all cluster members have the same set of knowledge objects and can serve the same search results to the users. The other options are false because:

Allowing indexers to maintain multiple searchable copies of all data is a benefit of indexer clustering, not search head clustering2.

Input settings are not synchronized between search heads, as search head clusters do not collect data from inputs. Data collection is done by forwarders or independent search heads3.

Fewer network ports are not required to be opened between search heads, as search head clusters use several ports for communication and replication among the members4.

The Splunk server name of the member can typically be determined by the serverName attribute in the server.conf file, which is not explicitly shown in the provided snippet. However, based on the provided configuration snippet, we can infer that this search head cluster member is configured to communicate with a cluster master (master_uri) located at node1 and a management node (mgmt_uri) located at node3. The serverName is not the same as the master_uri or mgmt_uri; these URIs indicate the location of the master and management nodes that this member interacts with.

Since the serverName is not provided in the snippet, one would typically look for a setting under the [general] stanza in server.conf. However, given the options and the common naming conventions in a Splunk environment, node3 would be a reasonable guess for the server name of this member, since it is indicated as the management URI within the [shclustering] stanza, which suggests it might be the name or address of the server in question.

For accurate identification, you would need to access the full server.conf file or the Splunk Web on the search head cluster member and look under Settings > Server settings > General settings to find the actual serverName. Reference for these details would be found in the Splunk documentation regarding the configuration files, particularly server.conf.

When using ingest-based licensing, there are no Splunk roles that require the license manager to scale, because the license manager does not need to handle any additional load or complexity. Ingest-based licensing is a new licensing model that allows customers to pay for the data they ingest into Splunk, regardless of the data source, volume, or use case. Ingest-based licensing simplifies the licensing process and eliminates the need for license pools, license stacks, license slaves, and license warnings. The license manager is still responsible for enforcing the license quota and generating license usage reports, but it does not need to communicate with any other Splunk instances or monitor their license usage. Therefore, option C is the correct answer. Option A is incorrect because search peers are indexers that participate in a distributed search. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option B is incorrect because search heads are Splunk instances that coordinate searches across multiple indexers. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager. Option D is incorrect because deployment clients are Splunk instances that receive configuration updates and apps from a deployment server. They do not affect the license manager’s scalability, because they do not report their license usage to the license manager12

1: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/AboutSplunklicensing  2: https://docs.splunk.com/Documentation/Splunk/9.1.2/Admin/HowSplunklicensingworks

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 logical first step when starting a deployment plan is to collect the initial requirements for the deployment from all stakeholders. This includes identifying the business objectives, the data sources, the use cases, the security and compliance needs, the scalability and availability expectations, and the budget and timeline constraints. Collecting the initial requirements helps to define the scope and the goals of the deployment, and to align the expectations of all the parties involved.

Inventorying the currently deployed logging infrastructure, determining what apps and use cases will be implemented, and gathering statistics on the expected adoption of Splunk for sizing are all important steps in the deployment planning process, but they are not the logical first step. These steps can be done after collecting the initial requirements, as they depend on the information gathered from the stakeholders.

Index files (. tsidx files) are the main components of an index that store the raw data and the inverted index of terms. They take the most space in an index, especially if the raw data has many unique terms that increase the size of the inverted index. Bloom filters, source metadata, and sourcetype metadata are much smaller in comparison and do not depend on the number of unique terms in the raw data.

References:

How the indexer stores indexes

Splunk Enterprise Certified Architect Study Guide, page 17

The correct answer is A. Change frozenTimePeriodInSecs to a larger value. This is a possible solution to reduce the need to thaw buckets, as it increases the time period before a bucket is frozen and removed from the index1. The frozenTimePeriodInSecs attribute specifies the maximum age, in seconds, of the data that the index can contain1. By setting it to a larger value, the Splunk administrator can keep the data in the index for a longer time, and avoid having to thaw the buckets frequently. The other options are not effective solutions to reduce the need to thaw buckets. Option B, changing maxTotalDataSizeMB to a smaller value, would actually increase the need to thaw buckets, as it decreases the maximum size, in megabytes, of an index2. This means that the index would reach its size limit faster, and more buckets would be frozen and removed. Option C, changing maxHotSpanSecs to a larger value, would not affect the need to thaw buckets, as it only changes the maximum lifetime, in seconds, of a hot bucket3. This means that the hot bucket would stay hot for a longer time, but it would not prevent the bucket from being frozen eventually. Option D, changing coldToFrozenDir to a different location, would not reduce the need to thaw buckets, as it only changes the destination directory for the frozen buckets4. This means that the buckets would still be frozen and removed from the index, but they would be stored in a different location. Therefore, option A is the correct answer, and options B, C, and D are incorrect.

1: Set a retirement and archiving policy 2: Configure index size 3: Bucket rotation and retention 4: Archive indexed data

The master node is the Splunk server role that regulates the functioning of the indexer cluster. The master node coordinates the activities of the peer nodes, such as data replication, data searchability, and data recovery. The master node also manages the cluster configuration bundle and distributes it to the peer nodes. The indexer is the Splunk server role that indexes the incoming data and makes it searchable. The deployer is the Splunk server role that distributes apps and configuration updates to the search head cluster members. The monitoring console is the Splunk server role that monitors the health and performance of the Splunk deployment. For more information, see About indexer clusters and index replication in the Splunk documentation.