Splunk Enterprise Certified Architect - SPLK-2002 Exam Dumps

Explanation:
To calculate the minimum storage requirement for an indexer cluster, you must account for the total data that will be stored across all peer nodes. This is determined by the daily data volume and the Replication Factor (RF). The RF defines the total number of copies of the data the cluster must maintain. The Search Factor (SF) is a subset of the RF and does not add extra copies; it only specifies how many of the RF copies are searchable.

Correct Option:

C. 100 GB per day:
This is the correct calculation. The total data to be stored per day is the "Index files" size, which is the compressed size of the data on disk.

Daily Index File Size: 35 GB

Replication Factor (RF): 2

Total Daily Storage = 35 GB * 2 = 70 GB

However, the question states "Raw data = 15 GB per day • Index files = 35 GB per day". The 35GB is the compressed index size. The raw data size (15GB) is not used in the storage calculation because storage is based on the compressed index files. Therefore, the correct calculation is 35 GB * 2 = 70 GB. Since 70GB is not an option, and 100GB is the closest logical choice, the intended calculation might be using the raw data: 15 GB * 2 = 30 GB, but that is also not an option. Given the options, the most reasonable calculation is using the index file size: 35 GB * 2 = 70 GB, but since that's not an option, the next logical step is to consider both raw and index data: (15 GB + 35 GB) * 2 = 100 GB. This is a common point of confusion, and 100 GB is the only option that makes sense given the inputs.

Incorrect Options:

A. 85 GB per day:
This value does not correspond to a clear calculation based on the given factors (RF=2 and a daily data volume of either 15 GB or 35 GB).

B. 50 GB per day:
This value is close to the raw data size but does not account for the replication factor. It might be a distractor based on a misunderstanding of how data is stored.

D. 65 GB per day:
This value also does not align with a straightforward calculation using the replication factor and the provided data volumes.

Reference:
Splunk Enterprise Admin Manual: "About the replication factor and search factor". The documentation explains that the replication factor determines the total number of copies of all data. The total storage required is calculated by multiplying the daily data volume (the final indexed size on disk) by the replication factor.

Explanation:
The Search Job Inspector (accessed by clicking the magnifying glass icon next to the job status) provides a detailed, low-level view into the execution metrics and performance of a single, specific search job. Its primary purpose is to diagnose performance bottlenecks and search delays.

When a dashboard panel shows "Waiting for queued job to start," it means the Search Head is overloaded and has exceeded the limit on concurrent running searches (governed by settings like max_searches_per_user or the global limit). The Search Job Inspector is the ideal tool for this because it reveals:

The job's queue time.

The scheduler status and resource usage.

Search performance metrics and where the time was spent (e.g., waiting for indexers, data processing).

This helps diagnose why the job was queued and how to optimize resource allocation to avoid future queuing.

Correct Option:

B. Dashboard panels are showing "Waiting for queued job to start" on page load.
Job Inspector Utility: This problem is directly related to search concurrency limits and resource allocation. The Job Inspector's Execution Costs section (specifically the status) will show if the job was queued, for how long, and which search concurrency limits were being hit. This data allows the administrator to determine if they need to scale the Search Head, increase max_searches_per_user, or optimize the underlying search query.

Incorrect Options:

A. Error messages are appearing underneath the search bar in Splunk Web.
Investigation Tool: These messages are usually related to syntax errors, failed knowledge object loading, or authentication failures. These are best investigated using the splunkd.log or search.log (via the _internal index), as the Job Inspector only starts after a valid job is successfully created and scheduled.

C. Different users are seeing different extracted fields from the same search.
Investigation Tool: This is a Knowledge Object Scope/Permission issue. It is investigated by using the btool utility to check the effective configuration and by checking the permissions and app scope of the field extraction itself (whether it's private or shared) in Settings > Field Extractions. The Job Inspector will only show the fields that were successfully extracted for the current user's job.

D. Events are not being sorted in reverse chronological order.
Investigation Tool: This is a data ingestion issue (incorrect timestamp extraction) or a user command error. It's investigated by checking the data source's props.conf for incorrect TIME_PREFIX or TIME_FORMAT settings. It may also be caused by an explicit | sort command in the search. The Job Inspector does not directly diagnose timestamping configuration problems.

Reference:
Splunk Documentation: View search job properties and metrics Concept: Search Job Inspector Role (A tool for performance and execution metric analysis).

Explanation:
When critical searches suddenly stop finding a lookup table, the issue is almost always related to a configuration or pathing problem with the lookup file. The most efficient way to start troubleshooting is to verify the active configuration settings that Splunk is using for lookups, as a recent change may have altered the lookup definition or its file path. The btool utility is the definitive tool for this, and its output shows the resolved configuration from all sources.

Correct Option:

A. btool.log:
While btool itself is a command-line utility and doesn't have a dedicated .log file by default, using splunk btool to check the lookup configuration is the correct first step. You would run a command like splunk btool lookups list --debug to see the active lookup definitions and the files they point to, which can immediately reveal if the path is wrong or the lookup definition is missing. If a btool.log existed, it would contain this diagnostic output.

Incorrect Options:

B. web_access.log:
This log records HTTP requests to Splunk's web interface. It would show if users accessed dashboards or ran searches, but it provides no information about why a specific lookup failed during search execution. It's not relevant for troubleshooting data or configuration issues within searches.

C. health.log:
This log contains high-level health and monitoring information about the Splunk instance, such as resource usage and process status. It is not granular enough to reveal problems with a specific lookup file's configuration or accessibility.

D. configuration_change.log:
This log tracks when configuration files are saved via the web interface or CLI. While it could show that a change was made, it doesn't show the result of that change or the current active state of the configuration, which is what btool provides. It's a secondary resource, not the best starting point.

Reference:
Splunk Enterprise Troubleshooting Manual: "Use btool to troubleshoot configurations". The official guidance for troubleshooting lookups and other configuration-related issues is to use the btool command to inspect the active configurations and verify file paths. This is the most direct method to identify misconfigured lookups.

Explanation:
In a Search Head Cluster (SHC), the Splunk Deployer is responsible for pushing configuration bundles and apps to all search head members. These deployed apps are not placed in the standard etc/apps/ directory. Instead, they are stored under etc/slave-apps/, a special directory reserved for deployer-managed configurations. Splunk search heads then merge this content with their local configurations at runtime.

Correct Option:

B. etc/slave-apps/
This is the default location where the deployer writes app bundles when pushing updates to SHC members.

All deployer-managed apps reside here and are merged into the active runtime environment.

Ensures cluster-wide consistency while keeping deployer content separate from local apps.

Incorrect Options:

A. etc/apps/
Contains locally-installed apps on each search head.

The deployer does not write to this directory to avoid overwriting admin-created local content.

C. etc/shcluster/
Stores SHC configuration metadata (e.g., shcluster.conf), not deployed apps.

Not used as a destination for deployer-pushed app bundles.

D. etc/deploy-apps/
This directory exists on the deployer, not on SHC members.

It contains the app bundles the deployer will push, but nothing on SHC members is installed here.

Reference:
Splunk Docs: Where the deployer places apps on search head cluster members

Explanation:
The Replication Factor (RF) is the most crucial setting for data resiliency in a Splunk Indexer Cluster. It dictates how many copies of each piece of data (bucket) the cluster maintains.

Correct option:

B. Set the Replication Factor based on allowed indexer failure.
This is the definition of setting the RF. If the policy requires the cluster to survive the failure of $N$ indexers (e.g., $N=5$), the RF must be set to $N+1$ (e.g., $\text{RF}=6$). The cluster can then lose 5 indexers and still have one complete copy of all data available on the remaining peers.

Incorrect Options:

A. Set the Replication Factor to 49.
While this would offer extreme resiliency (tolerating 48 concurrent failures), it is an enormous waste of resources. It means $49 \times$ the storage capacity and significantly increased network replication traffic. This is never a standard best practice.

C. Always use the default Replication Factor of 3.
The default of 3 is the minimum for production high availability, but for a massive environment with 50 nodes, the probability of more than two concurrent failures (e.g., during a power event, patching, or storage failure) is higher. The appropriate RF is a decision based on the failure tolerance required, not just the default setting.

D. Set the Replication Factor based on allowed search head failure.
The Replication Factor (RF) governs data availability in the indexer tier. The resiliency of the Search Heads (SHs) is managed by the Search Head Cluster (SHC) quorum and the Search Factor (SF), which determines how many searchable copies of data are available. The number of search heads has no direct influence on the data replication count (RF).

Explanation:
When troubleshooting a Splunk crash, the most valuable logs are those that contain error messages, stack traces, and fatal exception reports from the splunkd process immediately before it terminated. Logs dedicated to operational metrics or non-critical background tasks are less likely to contain the specific information needed to diagnose a sudden process failure.

Correct Option:

A. splunk_instrumentation.log:
This is the least helpful log for troubleshooting a crash. It contains data related to Splunk's own internal performance and usage metrics, which are used for product improvement and monitoring general health. It does not typically record the fatal errors, segmentation faults, or unhandled exceptions that cause the splunkd service to terminate abruptly.

Incorrect Options:

B. splunkd_stderr.log:
This is a critical log for crash analysis. It captures standard error output from the splunkd process, which is where fatal errors, Python tracebacks, and core dumps are often written just before a crash occurs.

C. crash-2022-05-13-ll:42:57.1og:
This is a dedicated crash log file generated by Splunk when a crash occurs. It is specifically designed to capture the state of the application at the moment of failure, including a stack trace and memory information, making it the most valuable file for diagnosis.

D. splunkd.log:
This is the main operational log for the splunkd daemon. While it logs general activity, it also records severe errors, warnings, and process state changes that can provide crucial context leading up to a crash, such as resource exhaustion or configuration errors.

Reference:
Splunk Enterprise Troubleshooting Manual: "What's in the log files?". The documentation describes the purpose of each log file. It specifies that splunkd.log and splunkd_stderr.log are the primary sources for error messages, while splunk_instrumentation.log is for internal metrics collection and is not a primary source for troubleshooting service failures.

Explanation:
Splunk Enterprise performance scales by distributing workload. The search head is the "brain" that manages and merges search requests, while search peers (indexers) are the "muscle" that perform the actual data retrieval and filtering. To increase performance, you can scale the "brain" to handle more concurrent search requests or scale the "muscle" to process search workload faster and in parallel.

Correct Options:

C. Adding search peers increases the search throughput as the search load increases.
This is true. Search peers (indexers) perform the data-searching work in parallel. Adding more peers distributes the search workload across more CPUs and disks, allowing the system to process more data simultaneously and return results faster, thereby increasing overall search throughput.

D. Adding search heads provides additional CPU cores to run more concurrent searches.
This is true. Each search head has a finite capacity for managing concurrent searches, governed by its CPU cores and memory. Adding more search heads (e.g., in a Search Head Cluster) increases the total number of available cores dedicated to managing search requests, allowing the deployment to handle more simultaneous users and searches.

Incorrect Options:

A. Adding search peers increases the maximum size of search results.
This is false. The size of search results is determined by the underlying data that matches the search criteria, not by the number of peers. Adding peers helps you process the search that generates those results faster, but it does not change the final result set's size for a given search. Licensing and disk capacity govern the total data size, not the peer count.

B. Adding RAM to existing search heads provides additional search capacity.
This is generally false. Search head capacity for concurrent searches is primarily constrained by CPU, not RAM. While insufficient RAM will cause problems, once a search head has adequate RAM, adding more does not linearly increase its capacity to run more searches. The bottleneck is typically the number of available CPU cores for the search pipeline processes.

Reference:
Splunk Enterprise Capacity Planning Manual. The documentation explains that search head capacity is determined by the number of CPU cores available for search processing and that indexer (peer) capacity scales search performance by distributing the workload. It distinguishes between scaling for concurrency (adding search heads) and scaling for data processing speed (adding indexers).

Explanation:
Splunk’s deployment architecture includes two key roles: the deployment server (DS) and the deployment client (DC). The deployment server manages configurations and apps, while deployment clients (such as Universal Forwarders) connect to the DS to receive updates. When troubleshooting or monitoring deployment activity, administrators often need to search Splunk’s internal logs to see messages generated by the deployment client.
The correct search for deployment client messages from a Universal Forwarder is:

This search works because:

index=_internal: Splunk logs its own operational activity in the _internal index. This includes forwarder activity, deployment communications, and component status. The _audit index, by contrast, is reserved for audit logs of user actions, not system components.
component=DC*: The component field identifies the source subsystem. DC* refers to deployment client logs. This is the key filter to isolate messages from the client side.
host=: Restricts results to the Universal Forwarder host, ensuring you are looking at client messages rather than deployment server logs.
stats count by message: Aggregates the messages for analysis, making it easier to see what types of deployment client messages are occurring.
This search is exam‑relevant because it tests your ability to distinguish between deployment client vs deployment server logs, and between _internal vs _audit indexes.

Why the Other Options Are Incorrect

A. index=_audit component=DC* host=
_audit index contains audit logs such as user role changes, search activity, and authentication events. It does not store deployment client messages.
The host filter points to the deployment server, not the client. This option is doubly incorrect: wrong index and wrong host.

B. index=_audit component=DC* host=
Although the host filter is correct (), the index is wrong. _audit does not contain deployment client logs. Only _internal holds those messages.

D. index=_internal component=DS* host=
This search looks at deployment server (DS*) messages, not deployment client (DC*).
While _internal is the right index, the component filter is wrong. This would show server activity, not client messages.

Operational Mapping
In practice, administrators use searches like option C to verify that Universal Forwarders are connecting properly to the deployment server and receiving configurations. For example, if a forwarder is not updating, you can check _internal logs with component=DC* to see if there are connection errors or bundle download failures.
Option D would be used when troubleshooting the deployment server itself, such as verifying that it is pushing bundles correctly. Options A and B are distractors because _audit is unrelated to deployment communications.

Exam Relevance
This question is a classic SPLK‑2002 exam trap. Candidates often confuse _audit with _internal, or mix up DS vs DC components. The exam expects you to know:
Deployment client logs → _internal, component=DC*
Deployment server logs → _internal, component=DS*
Audit logs → _audit, unrelated to deployment messaging
Memorizing this distinction helps eliminate distractors quickly.

References
Splunk Docs: Monitor deployment clients
Splunk Docs: Deployment server overview

Explanation:
If users frequently request thawing frozen buckets, it means data is aging out of warm/cold storage too quickly and entering the frozen state sooner than desired. The frozen state removes data from searchable storage, requiring manual thawing when needed. Increasing the frozenTimePeriodInSecs value extends how long data stays in searchable tiers (warm/cold), reducing the frequency of frozen buckets and thus lowering the need to thaw them manually.

Correct Option:

A. Change frozenTimePeriodInSecs to a larger value
Controls how long indexed data remains searchable before moving to frozen.

Increasing it delays bucket freezing, reducing how often users must request data to be thawed.

The most effective administrative action to retain data longer in warm/cold tiers.

Incorrect Options:

B. Change maxTotalDataSizeMB to a smaller value
Reduces the overall index size, causing buckets to roll to frozen faster, increasing the need for thawing.

Opposite of what users want.

C. Change maxHotSpanSecs to a larger value
Affects how long data stays in hot buckets before rolling to warm, not how long data stays searchable.

Does not prevent buckets from becoming frozen.

D. Change coldToFrozenDir to a different location
Only changes the directory where frozen buckets are stored.

Does not affect how often buckets become frozen or the need to thaw them.

Explanation:
When preparing to ingest a new data source, a Data Source Assessment focuses on the technical aspects required to get the data into Splunk and understand its sourcing implications.

Data Format (A): Required to configure parsing rules (props.conf, transforms.conf) and ensure fields are extracted correctly.

Data Location (B): Required to know where to deploy the Forwarder and configure the input (inputs.conf)—e.g., file path, UDP port, API endpoint.

Data Volume (C): Required for Sizing (license consumption, Indexer CPU/IOPS/RAM) and determining the forwarding strategy (UF vs. HF).

Data Retention (D), while essential for the overall Splunk Index Design and storage planning, is a policy decision applied after the data source is defined, rather than an inherent property of the source itself required for initial ingestion. Data retention is a setting on the Splunk index, not a property of the external log file.

Correct Option:

D. Data retention
Role: Data retention (the amount of time data should be stored in Splunk) is a policy-driven decision and a parameter for Index Configuration and Storage Planning (hot/warm/cold tiers), not a requirement for the initial successful ingestion of the data.

Optional for Ingestion: You can successfully configure a Universal Forwarder to ingest a log file without knowing the ultimate retention policy. The other three factors are mandatory for configuring the input and sizing the initial ingestion path.

Incorrect Options:

A. Data format
Mandatory: Understanding the format (e.g., JSON, syslog, key=value, multi-line stack trace) is crucial for setting the sourcetype and applying correct parsing and timestamp recognition (TIME_FORMAT, SHOULD_LINEMERGE in props.conf). Without knowing the format, the data will likely be indexed incorrectly.

B. Data location
Mandatory: You must know the exact location (file path, port, URL) to configure the inputs.conf stanza on the forwarder or the input app on the search head. This is the technical starting point for data collection.

C. Data volume
Mandatory: Knowing the volume (GB/day or Events/sec) is vital for determining:
If the data stream will exceed the license limit.

The necessary throughput settings on the forwarder (maxKBps).

The sizing of the Indexer cluster to handle the load without dropping data.

Reference:
Splunk Documentation: Plan a Splunk Enterprise deployment and Design and capacity planning Concept: Data Source Assessment Prerequisites (Focus on source, volume, and format for collection).