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Splunk Certified Cybersecurity Defense Engineer Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which features are crucial for validating integrations in Splunk SOAR? (Choose three)

A. Increasing indexer capacity
B. Testing API connectivity
C. Verifying authentication methods
D. Monitoring data ingestion rates
E. Evaluating automated action performance

Answer: B,C,E

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 # 24
Which configurations are required for data normalization in Splunk?(Choosetwo)

A. savedsearches.conf
B. props.conf
C. transforms.conf
D. authorize.conf
E. eventtypes.conf

Answer: B,C

Explanation:
Configurations Required for Data Normalization in Splunk
Data normalization ensures consistent field naming and event structuring, especially for Splunk Common Information Model (CIM) compliance.
#1. props.conf (A)
Defines how data is parsed and indexed.
Controls field extractions, event breaking, and timestamp recognition.
Example:
Assigns custom sourcetypes and defines regex-based field extraction.
#2. transforms.conf (B)
Used for data transformation, lookup table mapping, and field aliasing.
Example:
Normalizes firewall logs by renaming src_ip # src to align with CIM.
#Incorrect Answers:
C: savedsearches.conf # Defines scheduled searches, not data normalization.
D: authorize.conf # Manages user permissions, not data normalization.
E: eventtypes.conf # Groups events into categories but doesn't modify data structure.
#Additional Resources:
Splunk Data Normalization Guide
Understanding props.conf and transforms.conf

NEW QUESTION # 25
What cardinality of data should be used in an indexed field to optimize and speed up searches?

A. Compliant cardinality, meaning that only values that contain non-PII/PHI are contained in the field.
B. Low cardinality, meaning that there is little variance in the data contained in the field.
C. Secure cardinality, meaning that only security relevant values are contained in the field.
D. High cardinality, meaning that there is a great deal of variance in the data contained in the field.

Answer: B

Explanation:
To optimize and speed up searches, indexed fields should have low cardinality, meaning they contain relatively few unique values (e.g., status codes, country codes). Low cardinality fields are more efficient for indexing and searching compared to high cardinality fields with many unique values (like usernames or IP addresses).

NEW QUESTION # 26
An EDR tool was recently purchased and needs to be integrated into existing Splunk SOAR playbooks. Which actions are typically associated with this type of asset?

A. Block hash, block process, quarantine device, get indicator
B. Block hash, reset user password, quarantine device, get indicator
C. Block URL, block subdomain, quarantine device, get indicator, detonate URL
D. Block device, remove email, detonate URL, get indicator

Answer: A

Explanation:
EDR platforms commonly support host-level actions such as blocking malicious hashes, stopping or blocking processes, quarantining infected endpoints, and retrieving indicators for investigation.

NEW QUESTION # 27
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?

A. | eval field.mitre_attack.mitre_technique_id="T1027"
B. | eval annotations.mitre_attack.mitre_technique_id="T1027"
C. | set annotations.mitre_attack.mitre_technique_id="T1027"
D. | set field.mitre_attack.mitre_technique_id="T1027"

Answer: B

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 # 28
......

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