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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Detection Engineering | 40% | - Detection enrichment with context and risk-based alerting - Creation and tuning of detections (Correlation Searches) - Notable event generation and lifecycle management |
| Topic 2: Data Engineering | 10% | - Data ingestion and onboarding - Data parsing, normalization, and CIM alignment - Indexing performance and management |
| Topic 3: Security Operations and Program Development | 20% | - SOC process design and operational workflows - Threat intelligence integration |
| Topic 4: Security Automation (SOAR) | 30% | - Incident response automation and orchestration - Playbook design and automation workflows |
>> Exam Topics SPLK-5002 Pdf <<
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NEW QUESTION # 33
In which threat intelligence KV store would a list of malicious domains (FQDNs) be stored?
Answer: D
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 # 34
How can you ensure efficient detection tuning?(Choosethree)
Answer: A,B,C
Explanation:
Ensuring Efficient Detection Tuning in Splunk Enterprise Security
Detection tuning is essential to minimize false positives and improve security visibility.
#1. Perform Regular Reviews of False Positives (A)
Reviewing false positives helps refine detection logic.
Analysts should analyze past alerts and adjust correlation rules.
Example:
Tuning a failed login correlation search to exclude known legitimate admin accounts.
#2. Use Detailed Asset and Identity Information (B)
Enriches detections with asset and user context.
Helps differentiate high-risk vs. low-risk security events.
Example:
A login from an executive's laptop is higher risk than from a test server.
#3. Automate Threshold Adjustments (D)
Dynamic thresholds adjust based on activity baselines.
Reduces false positives while maintaining security coverage.
Example:
A brute-force detection rule dynamically adjusts its alerting threshold based on normal user behavior.
C: Disable correlation searches for low-priority threats # Instead of disabling, adjust the rule sensitivity or lower alert severity.
#Additional Resources:
Splunk Security Essentials: Detection Tuning Guide
Tuning Correlation Searches in Splunk ES
NEW QUESTION # 35
An engineer is writing a correlation search and wants to use T1027 from MITRE ATT&CK as a field in Incident Review. Assuming they are writing a correlation search that does not use the Risk data model, what example statement should be appended at the end of their correlation search?
Answer: C
Explanation:
To associate a MITRE ATT&CK technique with a correlation search that does not use the Risk data model, the correct approach is to append an eval statement that sets the annotation field.
The correct syntax is | eval annotations.mitre_attack.mitre_technique_id="T1027".
NEW QUESTION # 36
An effective method for building automation workflows is to follow the OODA (Observe, Orient, Decide, Act) loop stages. When transitioning between the Decide and Act stages, what additional step should be included before automating the Act stage?
Answer: C
Explanation:
Before moving from Decide into an automated Act operation, the workflow should determine whether the targeted asset, identity, account, or service is subject to an exemption . This represents an essential safety control for security automation.
The OODA model progresses from observation of an event, through contextual orientation and decision- making, toward action. The final transition can produce consequential operations such as disabling an account, isolating an endpoint, blocking an address, revoking credentials, or modifying infrastructure.
Performing those actions without checking exemptions can create significant business impact.
For example, automatically disabling a service account responsible for production authentication could cause an outage even though the detection itself was technically accurate. Exemption logic allows security engineers to establish guardrails for privileged systems, break-glass accounts, infrastructure services, executive assets, approved scanners, and other protected entities.
Creating a playbook or response template defines how automation is implemented, but neither substitutes for the decision guardrail immediately before execution. Effective security automation therefore combines machine-speed response with explicit environmental constraints.
The OODA automation question is presented on page 2 of the supplied certification material.
Study Guide topics: OODA, SOAR automation, automation guardrails, asset/identity exemptions, response safety, automated remediation.
NEW QUESTION # 37
How can an engineer verify if results will return for a potential detection based on historical events within the organization?
Answer: D
Explanation:
To verify if a potential detection will return results, the engineer should run the detection against production data in the same Splunk instance. This ensures the query is tested against actual historical events from the organization's environment, confirming whether it generates meaningful results.
NEW QUESTION # 38
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