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NEW QUESTION # 56
Once an engineer has determined that a new detection will fire, what is the next priority for that detection?
Answer: B
Explanation:
Once detection logic has been proven capable of firing under the intended conditions, the immediate engineering priority is ensuring that its output provides the context required by the analyst . A technically correct detection that generates a finding without useful investigative fields merely transfers work from the detection engineer to the SOC analyst.
Useful output may include fields such as user, src, dest, process information, timestamps, parent processes, URLs, authentication results, risk objects, or other contextual values appropriate to the detection. These fields allow an analyst to validate the activity, establish scope, determine severity, and choose an appropriate response without immediately rebuilding the originating search.
MITRE ATT & CK annotations are important for classification, reporting, and coverage analysis, while threat intelligence can enrich particular detections and SOAR can automate downstream response. However, those capabilities do not replace adequate detection output. Analyst usability logically precedes extensive enrichment and automation.
This reflects an important detection-engineering principle: detection efficacy is not simply whether a search generates an alert; the resulting security finding must be actionable and investigable .
Study Guide topics: Detection development, analyst workflow, actionable findings, contextual fields, detection validation, operationalization.
NEW QUESTION # 57
The following SPL is designed to report on a certain SOC metric. Which metric is the most likely topic for this report?
| tstats summariesonly=true earliest(_time) as _time
FROM datamodel=Incident_Management
BY " Notable_Events.Meta.rule_id "
| rename " Notable_Events.Meta.* " as " * "
| lookup update=true incident_updates_lookup rule_id OUTPUTNEW time
| search time=*
| stats earliest(_time) as create_time, min(time) as triage_time by rule_id
| eval diff=triage_time-create_time,
stat_type=if(
create_time < relative_time(now(), " -7d@d " ),
" past " ,
" current "
),
past=if(stat_type= " past " , 1, 0),
current=if(stat_type= " current " , 1, 0),
past_diff=if(stat_type= " past " , diff, 0),
current_diff=if(stat_type= " current " , diff, 0)
| stats sum(past) AS past,
sum(current) AS current,
sum(past_diff) AS past_diff,
sum(current_diff) AS current_diff
| eval past=round(past_diff/past/60),
current=round(current_diff/current/60)
| table past, current
| transpose
Answer: B
Explanation:
The SPL is measuring Mean Time to Triage because it calculates the elapsed time between creation of a notable event and the earliest recorded triage-related update.
The most important portion is:
| stats earliest(_time) as create_time, min(time) as triage_time by rule_id
| eval diff=triage_time-create_time
create_time represents when the notable was first generated, while triage_time is derived from the earliest applicable incident update. Subtracting the creation timestamp from the triage timestamp produces the amount of time analysts required to begin processing or triaging the finding.
The search then categorizes records into past and current periods, calculates average differences, and converts those values into minutes. This permits comparison of SOC triage performance across reporting periods.
Mean Time to Respond would require a timestamp representing a response action. Mean Time to Resolve requires closure or resolution information. Dwell Time measures how long adversary activity remains present or undetected and is therefore conceptually different from the notable-event lifecycle calculation shown.
Study Guide topics: SOC metrics, Mean Time to Triage, Incident Management data model, notable lifecycle, incident_updates_lookup, operational performance reporting.
NEW QUESTION # 58
Which features are crucial for validating integrations in Splunk SOAR? (Choose three)
Answer: A,B,C
Explanation:
Validating Integrations in Splunk SOAR
Splunk SOAR (Security Orchestration, Automation, and Response) integrates with various security tools to automate security workflows. Proper validation of integrations ensures that playbooks, threat intelligence feeds, and incident response actions function as expected.
#Key Features for Validating Integrations
1##Testing API Connectivity (A)
Ensures Splunk SOAR can communicate with external security tools (firewalls, EDR, SIEM, etc.).
Uses API testing tools like Postman or Splunk SOAR's built-in Test Connectivity feature.
2##Verifying Authentication Methods (C)
Confirms that integrations use the correct authentication type (OAuth, API Key, Username/Password, etc.).
Prevents failed automations due to expired or incorrect credentials.
3##Evaluating Automated Action Performance (D)
Monitors how well automated security actions (e.g., blocking IPs, isolating endpoints) perform.
Helps optimize playbook execution time and response accuracy.
#Incorrect Answers & Explanations
B: Monitoring data ingestion rates # Data ingestion is crucial for Splunk Enterprise, but not a core integration validation step for SOAR.
E: Increasing indexer capacity # This is related to Splunk Enterprise data indexing, not Splunk SOAR integration validation.
#Additional Resources:
Splunk SOAR Administration Guide
Splunk SOAR Playbook Validation
Splunk SOAR API Integrations
NEW QUESTION # 59
An engineer adds a custom event status of ' Testing ' and accidentally makes it the new default status.
Their SOC calculates some metrics based on Notable status change sequences, starting from the old default status of ' New ' . Which metrics can be affected by this mistake?
Answer: A
Explanation:
The affected metrics are Mean Time to Respond and Mean Time to Resolve because both can depend on the expected lifecycle of a notable beginning in the default New state and progressing through subsequent analyst-handling states.
If Testing is accidentally configured as the default, newly generated notables no longer begin with the status value expected by searches or reports that identify sequences such as:
New # In Progress
or
New # ... # Resolved
As a result, the timestamp used to establish the beginning of the response or resolution interval may be missing from the expected status-change sequence, producing inaccurate, incomplete, or excluded metric calculations.
Dwell Time is different. It measures how long malicious activity remains present or undetected in the environment and is not fundamentally calculated from Enterprise Security notable-status transitions. That eliminates options C and D. Option B is also incorrect because changing a status value relied upon by existing SOC metric searches can directly alter their results.
The supplied guide explicitly emphasizes notable status and ownership as operational SOC measurement fields , supporting the importance of preserving lifecycle-state consistency.
Study Guide topics: notable-event lifecycle, status transitions, Mean Time to Respond, Mean Time to Resolve, SOC metrics, reporting consistency.
NEW QUESTION # 60
The Director of Security would like to understand the operational efficiency of the SOC analysts at a high level. What is a metric that can be used to determine their efficiency?
Answer: C
Explanation:
Mean Time to Respond (MTTR) measures how quickly SOC analysts take action after an alert is identified. It is a key high-level indicator of SOC operational efficiency.
NEW QUESTION # 61
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