CCNA Siem Defenses And Data Practices Questions

Mapping sourcetypes to data models is a core CIM Setup task: the interface binds each sourcetype's fields to the Common Information Model's normalised schema, so that data model acceleration and downstream correlation searches return consistent, vendor-neutral results across heterogeneous sources.

Identity Investigator provides a unified view of a user's activity across various data sources, including authentication and VPN logs. It is specifically designed to help analysts quickly assess user behavior and investigate notable events related to identities, making it the correct tool for this scenario.

Data Model Acceleration precomputes and stores summaries of indexed data, letting correlation searches query those summaries instead of scanning raw events across indexes. This satisfies the stem's large-dataset efficiency requirement, since correlation speed no longer depends on raw index volume.

The Incident Review dashboard in Splunk Enterprise Security is designed for triaging notable events. It allows analysts to filter by owner, status, urgency, and other fields, and to take actions such as assigning, commenting, and closing notables. This dashboard is the central place for managing notable events and is exactly where an analyst would go to see their open assignments.

Optimising the query and using data model summaries directly reduces the data scanned, satisfying the stem's excessive-scan constraint. Data model summaries pre-compute aggregated results, so the search reads accelerated summary data rather than raw events, cutting scan volume and resolving the timeout.

Incident Review provides the central console where analysts triage notable events, changing status, owner, urgency and disposition. This workflow directly manages each notable's lifecycle from detection through assignment to closure, satisfying the requirement to govern event handling.

Assigning a security domain (such as Access, Endpoint, Network, etc.) helps categorize the notable events and enables filtering and reporting in the Incident Review dashboard. It is a recommended practice to ensure that the events are properly classified for later analysis and response. This action directly supports tracking and categorization.

An Adaptive Response action only fires when explicitly enabled; a disabled action remains configured but never executes. This satisfies the stem's requirement for a mandatory component, alongside a triggering correlation search and a defined response action type.

Placing the script in the app's bin directory satisfies Splunk's requirement that custom Adaptive Response scripts reside within the app's own directory structure. Splunk executes binaries from `$SPLUNK_HOME/etc/apps/<app_name>/bin`, so this path guarantees the Python script is discoverable and runnable by the instance.

Mapping the notable event's owner field to an extracted user field assigns each event to the relevant user, so distinct users triggering the same correlation search retain correct ownership instead of a single static value.

The Retention Period setting controls how long notable events persist in the notable index before ageing out. It governs storage duration only, not search scheduling or correlation logic, satisfying the requirement to determine how long notable event data remains available.

The Risk Analysis framework in Splunk ES allows you to assign risk scores to entities (users, systems, etc.) based on events. It uses risk rules to calculate and modify risk scores, which can then be used in correlation searches to trigger notables when a threshold is exceeded. This is the correct feature for automatically adjusting risk scores based on events. It provides a dynamic risk posture and is integral to risk-based alerting.

The Assets and Identities framework enriches security events with contextual metadata such as department, role, priority and business unit, satisfying the stem's requirement to explain its purpose. This context lets Splunk ES correlate notable events against asset criticality and identity attributes, improving risk scoring and investigation triage.

Reducing the cron schedule directly lowers how often the correlation search executes, cutting cumulative CPU and memory consumption across the search head. Since resource exhaustion stems from repeated concurrent execution, widening the interval between runs satisfies the stem's constraint without altering detection logic or throttling individual alerts.

A Risk Object normalises alerts from different correlation searches onto one entity, such as a user or host, so Splunk ES aggregates their risk scores. This lets analysts see cumulative risk per entity rather than isolated alerts, which is the framework's core purpose.

A lookup definition allows the correlation search to reference the CSV file as a lookup table. By using the lookup command, the search can match the source IP against the list and filter out known internal IPs. This is the standard method in Splunk ES for incorporating external lists into correlation searches, enabling dynamic updates without editing the search itself.

Data model acceleration is required to precompute results and improve search performance. Without acceleration, the data model is not materialized, and searches run against raw data, leading to slower performance. Enabling acceleration on the data model allows the correlation search to leverage the precomputed summaries, significantly reducing search time.

Asset and identity lookups enrich raw events with ownership, priority and behavioural context, letting correlation searches and risk scoring distinguish critical systems from noise. Without this contextual data, ES cannot meaningfully prioritise or attribute the activity it detects, so insights remain shallow.

Consistent source data ensures enrichment lookups resolve reliably, since Splunk correlates assets and identities by matching field values across lookups. Inconsistent naming, formatting or missing identifiers cause failed joins, producing incomplete or inaccurate enrichment. This directly satisfies the stem's requirement for effective Asset and Identity enrichment, where trustworthy, uniform source files underpin every correlation search.

Dollar-sign tokens substitute field values from the triggering event into the drilldown URL at render time, letting the target page receive the exact field content. This is the documented token syntax for notable event drilldowns.

Urgency in Splunk ES is calculated from the notable event's risk score, which aggregates risk from contributing risk objects and anomaly detections. A higher risk score directly raises urgency, so this factor satisfies the stem's requirement for a direct input to the urgency calculation.

The CIM Validator app inspects indexed events against a selected data model and reports which fields fail to map, exposing missing or misnamed CIM fields. This satisfies the stem's efficiency requirement by diagnosing mapping gaps before any manual field extraction work.

The Malware data model in the Common Information Model is specifically designed for malware detection events, including infections, quarantines, and cleanups. Mapping endpoint protection logs to this data model ensures that the events are normalized and available for correlation searches and dashboards that rely on malware data. This is the correct data model for the scenario.

The 'notable' index stores events generated when correlation searches trigger, revealing whether the search fired and what it produced. Inspecting it confirms detection, timestamps and field values, satisfying the need to verify whether a malfunctioning search executed at all before investigating its underlying SPL logic.

The Threat Intelligence Manager dashboard in Splunk Enterprise Security lists configured intelligence sources and lets analysts review, enable, or disable feeds. It is the designated interface for managing active threat intelligence feeds, matching the question's requirement precisely.

Adaptive Response actions execute through the specific add-on managing that device, so its configuration supplies the connectivity and credentials used for remote execution. Checking these first satisfies the stem's requirement to verify the action's own transport and authentication path before investigating broader platform or network issues.

The alert action settings determine what happens when the search triggers. For notable events to appear in Incident Review, the search must have the 'Notable' alert action enabled and properly configured. If this action is missing or misconfigured, the search results will not generate notables, even if the search runs successfully. This is the first thing to verify.

The correlation search defines a risk factor, and each notable event it generates inherits that value as its Risk Score. This satisfies the question by identifying the configured risk factor, not the event severity or asset criticality, as the origin.

The lookup command matches event fields against an external KV Store collection holding indicators, appending threat context such as severity or category to each event. This satisfies the enrichment requirement without indexing new data, since the intelligence is referenced at search time.

The Splunk Add-on for Microsoft Windows ships the CIM-compliant field extractions and tags that map Windows Event Log data to the Authentication data model. Configuring the sourcetype within its input configuration ensures events are correctly tagged and aliased, satisfying the CIM conformance requirement.

The `lookup` command enriches events by matching field values against an external table, such as a threat intelligence CSV or KV Store collection. This directly satisfies the requirement to join threat intelligence data with local search results, appending indicators like IP reputation or file hashes to each matching event.

Alert suppression allows you to suppress duplicate notables based on specified fields. Setting the suppression field to user and the period to 24 hours ensures that once a notable is generated for a user, subsequent triggers for that same user are suppressed for 24 hours, preventing duplicate notables. This directly meets the requirement.

Notable Event Suppression applies suppression rules that filter out notables matching known-benign criteria, such as expected scanner activity. This reduces alert fatigue from known false positives, satisfying the stem's requirement by preventing repetitive, low-value notables from reaching analysts.

The CIM Add-on normalises disparate source types by mapping their fields to the Common Information Model's unified schema, satisfying the stem's requirement for a single consistent data model across heterogeneous logs. It performs field aliasing and event tagging at search time, letting correlation searches reference standardised field names regardless of the originating sourcetype.

Running the correlation search's query logic manually in the Search app isolates whether the underlying search returns results at all. If it produces events, the logic is sound and the fault lies in scheduling, throttling or alert configuration rather than the query itself.

Correlation searches run on the search head, and their alert actions create the notable events surfaced in Incident Review. The search head is therefore the component that generates notables, satisfying the stem's requirement for the generating component within the Splunk Enterprise Security architecture.

Acceleration requires both enabling it on the data model and defining a summary range, which determines how far back summaries are built. Without a summary range, acceleration cannot populate correctly, so this action satisfies the stem's requirement for notable event generation.

The Asset and Identity framework correlates event fields such as IP, hostname or user against lookup collections, appending asset and identity metadata to notable events during correlation search execution. This satisfies the enrichment requirement by resolving raw identifiers into contextual ownership and priority data.

Notable Event Suppression matches specific field values, such as the scanner's signature or source, and drops only those matching events before they become notables. The correlation search keeps running, satisfying the requirement to suppress without disabling it.

Tags are required to associate events with specific data models and event types within the CIM. For instance, tagging authentication events with 'authentication' and 'success' allows the Authentication data model to include them. Tags are applied via the 'tag' command in props.conf or through the UI. Without correct tags, the data model will not accelerate or include the events, breaking CIM-based correlations.

Risk score adjustments for a specific asset are applied through the risk scoring configuration or by editing lookup tables that map asset attributes to scores. This overrides the default calculation without altering global correlation searches.

Urgency sets the severity value assigned to the notable event, which drives its priority ranking and sorting within the Incident Review dashboard. Analysts triage by this ordering, so it satisfies the requirement to determine how prominently the notable appears.

A correlation search only creates a notable when its alert action is configured to trigger the 'Notable' adaptive response action. Without that action enabled, results appear in search but never reach the notable index, so nothing shows in Incident Review despite the search returning matches.

A custom alert action is required to integrate with a third-party ticketing system. Splunk ES allows the creation of custom alert actions using the Splunk SDK or REST API, which can be triggered by the correlation search. This enables automatic ticket creation when the notable event is generated, streamlining incident response and ensuring no critical alerts are missed.

Setting the TZ attribute in props.conf for the affected sourcetype corrects timestamp interpretation at index time, aligning source data with Splunk's expected time zone. This resolves the mismatch preventing correlation searches from generating notable events.

Data Models in Splunk ES are used to normalize data by defining a common set of fields and applying field aliases and calculations. The Common Information Model (CIM) is implemented through data models, which map vendor-specific fields to standardized field names like 'user'. This allows correlation searches and dashboards to work across different data sources without modification. Data Models are the core component for normalization in Splunk ES, making this the correct answer.

Splunk ES provides a configuration page for Incident Review table attributes where administrators can specify which fields appear as columns. By adding 'src_ip' to the list of attributes, it will be displayed in the table. This is the correct and supported method to customize the Incident Review dashboard's columns without modifying underlying searches or dashboards.

Acceleration pre-computes data model summaries, enabling faster searches. Without acceleration, the correlation search may exceed time limits or fail to process large datasets, leading to no notables. In Splunk ES, many correlation searches depend on accelerated data models for performance and reliability. Thus, enabling acceleration on the Authentication data model is critical for this search to function.

Owner filtering restricts the notable event list to those assigned to a specific analyst, supporting workload management and handover. It satisfies the stem's requirement because owner is a standard, editable field on notable events and appears as a built-in filter in the Incident Review dashboard.

The Incident Review dashboard is the primary triage workspace in Splunk Enterprise Security, letting analysts assign, prioritise and track notable events through their lifecycle. It satisfies the stem's focus on purpose by centralising notable event management, rather than providing correlation search configuration or data model acceleration.

Analysts can manually create incidents in Incident Review, and these are stored and treated as notable events alongside correlation search output. This satisfies the requirement by covering the human-generated category, distinguishing it from automated detections and ordinary indexed events.

The Risk Analysis framework in Splunk ES collects risk events from various sources into the 'risk' index. A correlation search can then search the 'risk' index, aggregate risk scores by user, and trigger a notable when the threshold is exceeded. This is the intended use of the framework, which provides a centralized way to manage risk-based alerting. The correlation search should use the 'risk' index and sum the risk_score field.

The Risk Analysis adaptive response action writes a risk event to the risk index, recording a risk score against a named risk object such as a user, host or system. This attribution lets subsequent risk notables aggregate and prioritise by entity.