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NEW QUESTION # 18
Which Splunk feature helps in tracking and documenting threat trends over time?
Answer: B
Explanation:
Why Use Risk-Based Dashboards for Tracking Threat Trends?
Risk-based dashboards in Splunk Enterprise Security (ES) provide a structured way to track threats over time.
#How Risk-Based Dashboards Help:#Aggregate security events into risk scores # Helps prioritize high-risk activities.#Show historical trends of threat activity.#Correlate multiple risk factors across different security events.
#Example in Splunk ES:#Scenario: A SOC team tracks insider threat activity over 6 months.#The Risk-Based Dashboard shows:
Users with rising risk scores over time.
Patterns of malicious behavior (e.g., repeated failed logins + data exfiltration).
Correlation between different security alerts (e.g., phishing clicks # malware execution).
Why Not the Other Options?
#A. Event sampling - Helps with performance optimization, not threat trend tracking.#C. Summary indexing
- Stores precomputed data but is not designed for tracking risk trends.#D. Data model acceleration - Improves search speed, but doesn't track security trends.
References & Learning Resources
#Splunk ES Risk-Based Alerting Guide: https://docs.splunk.com/Documentation/ES#Tracking Security Trends Using Risk-Based Dashboards: https://splunkbase.splunk.com#How to Build Risk-Based Analytics in Splunk: https://www.splunk.com/en_us/blog/security
NEW QUESTION # 19
In which threat intelligence KV store would a list of malicious domains (FQDNs) be stored?
Answer: A
Explanation:
The correct collection is service_intel . Splunk Enterprise Security ' s Threat Intelligence Framework separates indicators into intelligence collections according to the type of observable being represented. Fully Qualified Domain Names are service-oriented network identifiers and are handled through the service intelligence collection in the context tested by this question.
This classification matters because the Threat Intelligence Framework must know which event fields and indicator types can be meaningfully compared. A domain such as malicious.example.com is semantically different from a raw IPv4/IPv6 address, a certificate fingerprint, or a complete HTTP URL. The ip_intel collection is intended for IP-oriented indicators, while certificate_intel deals with certificate-related intelligence. http_intel is associated with HTTP-oriented indicators such as URLs and related HTTP observables rather than the standalone FQDN type being asked about here.
Detection engineering depends on this normalization because matching searches must compare compatible indicator types. Correct placement also supports deduplication, expiration, weighting, threat matching, and downstream enrichment of security findings.
Study Guide topics: Threat Intelligence Framework, KV Store collections, indicator normalization, FQDN intelligence, threat matching, intelligence enrichment.
NEW QUESTION # 20
When developing security metrics, why would a Key Performance Indicator (KPI) that focuses on total perimeter firewall blocks be an ineffective metric?
Answer: D
Explanation:
A raw count of firewall blocks measures an output or result , not whether the security capability itself is performing effectively. Under the terminology used by the supplied course material, this makes the measurement a Key Result Indicator rather than a Key Performance Indicator .
For example, a perimeter firewall could block ten million connection attempts because the public-facing address space is being heavily scanned. A second firewall might block only one million. Those numbers alone do not establish that the first firewall is ten times more effective. External scanning volume, Internet exposure, business architecture, and traffic characteristics can change the count independently of firewall performance.
A useful KPI should instead measure performance against a defined operational objective-for example, response latency, policy deployment accuracy, control availability, processing performance, or another measurable objective tied to effectiveness. The total-block count can still provide useful operational context, but it should not automatically be interpreted as evidence that the firewall program is improving.
Option D captures precisely this distinction between activity/result volume and control performance .
Study Guide topics: security metrics, KPIs, result indicators, control effectiveness, SOC reporting, meaningful measurement.
NEW QUESTION # 21
What provides consistency for data mapping applied to data model and saved search exports between Splunk Enterprise Security and Splunk SOAR?
Answer: A
Explanation:
Global field mappings provide a consistent mapping layer when data is exported from Enterprise Security data models or saved searches into Splunk SOAR. The objective is to ensure that semantically equivalent values are represented consistently regardless of which detection, search, or data model produced the source event.
This becomes important when SOAR receives security events from multiple searches. One source might expose an endpoint as dest, another as host, and another might use a product-specific field. Consistent global mapping allows downstream SOAR workflows to operate on standardized field semantics instead of requiring every playbook to understand every upstream schema variation.
A field alias is a Splunk knowledge object that can make one indexed or extracted field available under another name during search processing, but that is not the cross-export mapping mechanism requested here.
"Field labels" likewise do not establish the global ES-to-SOAR translation layer. The important clue is the requirement for consistency across both data-model and saved-search exports , which implies a centrally applied mapping configuration.
This design reduces duplicated transformation logic and improves the portability and reliability of automated response workflows.
Study Guide topics: Global field mappings, Enterprise Security-to-SOAR integration, data normalization, event export, automation data consistency.
NEW QUESTION # 22
What methods improve risk and detection prioritization?(Choosethree)
Answer: A,C,D
Explanation:
Risk and detection prioritization in Splunk Enterprise Security (ES) helps SOC analysts focus on the most critical threats. By assigning risk scores, integrating business context, and automating detection tuning, organizations can prioritize security incidents efficiently.
Methods to Improve Risk and Detection Prioritization:
Assigning Risk Scores to Assets and Events (A)
Uses Risk-Based Alerting (RBA) to prioritize high-risk activities based on behavior and history.
Helps SOC teams focus on true threats instead of isolated events.
Incorporating Business Context into Decisions (C)
Adds context from asset criticality, user roles, and business impact.
Ensures alerts are ranked based on their potential business impact.
Automating Detection Tuning (D)
Uses machine learning and adaptive response actions to reduce false positives.
Dynamically adjusts alert thresholds based on evolving threat patterns.
NEW QUESTION # 23
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