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NEW QUESTION # 63
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 KPI based on total perimeter firewall blocks is ineffective because perimeter firewalls are constantly exposed to the internet and subject to automated scans and attack tools, which can generate very high block counts. This inflates the metric with noise, making it a poor indicator of actual security performance or risk reduction.
NEW QUESTION # 64
How can you ensure efficient detection tuning?(Choosethree)
Answer: B,C,D
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 # 65
Which tool can help identify known tactics, techniques, and procedures that a threat group is most likely to use when targeting a financial organization?
Answer: A
Explanation:
The MITRE ATT & CK matrix ' s industry heatmap in Splunk Security Essentials is designed to help security teams understand which ATT & CK techniques are particularly relevant to specific industries. For a financial organization, this capability provides an industry-oriented view that assists engineers in identifying adversary behaviors and prioritizing detection coverage accordingly.
MITRE ATT & CK organizes adversarial behavior into tactics and techniques, while Splunk Security Essentials provides security-content and coverage context that can be related to those behaviors. An industry heatmap enables engineers to move beyond treating every ATT & CK technique as equally significant and instead examine techniques associated with threats relevant to their business sector.
This can support detection-program planning by exposing coverage gaps, prioritizing new analytics, and aligning threat hunting with realistic adversarial behavior. The result is a threat-informed detection strategy grounded in likely TTPs rather than an undifferentiated list of techniques.
The Mission Control and Incident Review distractors describe interfaces used for handling findings rather than the industry-focused ATT & CK analysis capability. Splunk Threat Intelligence Management manages threat intelligence but does not represent the specific industry heatmap requested.
Study Guide topics: Splunk Security Essentials; MITRE ATT & CK; industry heatmaps; TTPs; detection coverage; threat-informed defense; security-program prioritization.
NEW QUESTION # 66
An engineer has been working on building a new automation for the SOC. What Scope should be selected in the SOAR Playbook Debugger during the playbook development to ensure consistency?
Answer: A
Explanation:
During playbook development and debugging, All Events provides the most consistent test scope because the engineer can repeatedly execute and evaluate the playbook against available event/container data rather than depending exclusively on events created after the playbook was activated.
A scope such as New Events is more appropriate for operational execution when the automation should trigger only as new events arrive. During development, however, restricting execution to newly created objects can make testing inconsistent: an engineer may need to repeatedly create fresh events simply to reproduce the same test case.
The artifact-oriented choices apply when execution is specifically driven by artifact creation or artifact-level behavior. They are not the broad event-level scope requested for consistent playbook debugging.
Using a stable test event is particularly useful when validating branching logic, asset actions, enrichment results, API responses, and downstream automation because the same input can be replayed while the engineer changes individual portions of the playbook.
The supplied study set discusses SOAR playbooks, containers, automation scope, and development workflows, although this exact debugger-scope question is not included verbatim.
Study Guide topics: Splunk SOAR Playbook Debugger, playbook scope, event processing, repeatable testing, automation development.
NEW QUESTION # 67
When should a detection be reviewed or retuned after deployment?
Answer: B
Explanation:
Detection review and tuning should occur according to the organization ' s established detection lifecycle , rather than only in response to a single symptom or an arbitrary universal interval.
A mature detection lifecycle defines how security content progresses through design, development, testing, deployment, validation, operational monitoring, and subsequent tuning or maintenance. Post-deployment review should consider multiple signals: false-positive volume, unexpected false negatives, changes in telemetry, new application behavior, data-model changes, threat-intelligence developments, ATT & CK coverage requirements, analyst feedback, and changes in the environment.
Option A imposes a fixed 30-day interval without considering the organization ' s lifecycle or risk requirements. A monthly review may be appropriate in a particular program, but it is not universally required.
Option B is too narrow because a detection may require tuning even when false positives are low-for example, if required fields disappear or attacker behavior changes. Option D is similarly incomplete because an absence of findings does not automatically prove that the detection is ineffective.
The supplied material explicitly tests the Detection Development Lifecycle (DDLC) and emphasizes structured detection development rather than ad hoc changes.
Study Guide topics: DDLC, detection validation, post-deployment tuning, lifecycle governance, false- positive management, detection maintenance.
NEW QUESTION # 68
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