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Exam Splunk SPLK-5002 Exercise | Valid SPLK-5002 Exam Discount
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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q34-Q39):
NEW QUESTION # 34
What is the main benefit of automating case management workflows in Splunk?
- A. Reducing response times and improving analyst productivity
- B. Enabling dynamic storage allocation
- C. Minimizing the use of correlation searches
- D. Eliminating the need for manual alerts
Answer: A
Explanation:
Automating case management workflows in Splunk streamlines incident response and reduces manual overhead, allowing analysts to focus on higher-value tasks.
Main Benefits of Automating Case Management:
Reduces Response Times (C)
Automatically assigns cases to analysts based on predefined rules.
Triggers playbooks and workflows in Splunk SOAR to handle common incidents.
Improves Analyst Productivity (C)
Reduces time spent on manual case creation and updates.
Provides integrated case tracking across Splunk and ITSM tools (e.g., ServiceNow, Jira).
NEW QUESTION # 35
What is the primary purpose of developing security metrics in a Splunk environment?
- A. To enhance data retention policies
- B. To measure and evaluate the effectiveness of security programs
- C. To identify low-priority alerts for suppression
- D. To automate case management workflows
Answer: B
Explanation:
Security metrics help organizations assess their security posture and make data-driven decisions.
Primary Purpose of Security Metrics in Splunk:
Measure Security Effectiveness (B)
Tracks incident response times, threat detection rates, and alert accuracy.
Helps SOC teams and leadership evaluate security program performance.
Improve Threat Detection & Incident Response
Identifies gaps in detection logic and false positives.
Helps fine-tune correlation searches and notable events.
NEW QUESTION # 36
Which Splunk configuration ensures events are parsed and indexed only once for optimal storage?
- A. Universal forwarder
- B. Index time transformations
- C. Summary indexing
- D. Search head clustering
Answer: B
Explanation:
Why Use Index-Time Transformations for One-Time Parsing & Indexing?
Splunk parses and indexes data once during ingestion to ensure efficient storage and search performance.
Index-time transformations ensure that logs are:
#Parsed, transformed, and stored efficiently before indexing.#Normalized before indexing, so the SOC team doesn't need to clean up fields later.#Processed once, ensuring optimal storage utilization.
#Example of Index-Time Transformation in Splunk:#Scenario: The SOC team needs to mask sensitive data in security logs before storing them in Splunk.#Solution: Use anINDEXED_EXTRACTIONSrule to:
Redact confidential fields (e.g., obfuscate Social Security Numbers in logs).
Rename fields for consistency before indexing.
NEW QUESTION # 37
What are the main steps of the Splunk data pipeline?(Choosethree)
- A. Alerting
- B. Visualization
- C. Input phase
- D. Parsing
- E. Indexing
Answer: C,D,E
Explanation:
The Splunk Data Pipeline consists of multiple stages that process incoming data from ingestion to visualization.
Main Steps of the Splunk Data Pipeline:
Input Phase (C)
Splunk collects raw data from logs, applications, network traffic, and endpoints.
Supports various data sources like syslog, APIs, cloud services, and agents (e.g., Universal Forwarders).
Parsing (D)
Splunk breaks incoming data into events and extracts metadata fields.
Removes duplicates, formats timestamps, and applies transformations.
Indexing (A)
Stores parsed events into indexes for efficient searching.
Supports data retention policies, compression, and search optimization.
NEW QUESTION # 38
What are the benefits of maintaining a detection lifecycle?(Choosetwo)
- A. Automating the deployment of new detection logic
- B. Scaling the Splunk deployment effectively
- C. Ensuring detections remain relevant to evolving threats
- D. Detecting and eliminating outdated searches
Answer: C,D
Explanation:
Why Maintain a Detection Lifecycle?
Adetection lifecycleensures that security alerts, correlation searches, and automation playbooks arecontinuously refinedto maintainaccuracy, efficiency, and relevanceagainst modern threats.
#1. Detecting and Eliminating Outdated Searches (Answer A)#Removes unnecessary or redundant correlation searchesthat may slow down performance.#Prevents false positivescaused by outdated detection logic.
#Example:A Splunk ES search for anold malware variantmay no longer be effective # it should be updated to detectnew techniques used by attackers.
#2. Ensuring Detections Remain Relevant to Evolving Threats (Answer C)#Regular updatesensure thatnew MITRE ATT&CK techniquesand threat indicators are included.#Example:If attackers start usingLiving-off- the-Land (LotL) techniques, security teams mustupdate detection rules to identify suspicious PowerShell activity.
Why Not the Other Options?
#B. Scaling the Splunk deployment effectively- Lifecycle management improvesdetection accuracy, notinfrastructure scalability.#D. Automating the deployment of new detection logic- Automation helps, but lifecycle management isabout reviewing and updating detections, not just deployment.
References & Learning Resources
#Detection Management in Splunk ES: https://docs.splunk.com/Documentation/ES#Updating Threat Detections Using MITRE ATT&CK in Splunk: https://attack.mitre.org/resources#Best Practices for SOC Detection Engineering: https://splunkbase.splunk.com
NEW QUESTION # 39
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