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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk Certified Cybersecurity Defense Engineer (CDE) |
| Exam Number: | SPLK-5002 |
| Related Certifications: | Splunk Certified Cybersecurity Defense Analyst |
| Certificate Validity Period: | Not publicly specified |
| Exam Format: | Multiple choice, Scenario-based multiple choice |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Exam Price: | $130 USD |
| Exam Duration: | 75 minutes |
| Recommended Training: | Splunk Enterprise Security Fundamentals Splunk SOAR Automation Training |
| Exam Registration: | Pearson VUE Splunk Exams Official Splunk Certification Registration |
| Sample Questions: | Splunk SPLK-5002 Sample Questions |
| Exam Way: | Online proctored or test center (Pearson VUE) |
| Pre Condition: | No formal prerequisites required, but Splunk Certified Cybersecurity Defense Analyst knowledge is strongly recommended. |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification-track/splunk-certified-cybersecurity-defense-engineer.html |
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NEW QUESTION # 60
What is the best method to operationalize the results of a threat hunt for daily use by SOC analysts?
Answer: B
Explanation:
The best way to operationalize the results of a threat hunt is to create detections based on the documented findings. This transforms hunting insights into actionable, repeatable detection logic that SOC analysts can use daily to identify similar threats in real time.
NEW QUESTION # 61
Based on the provided screenshot, different machines or accounts have been associated with chosen threat objects. Which two Enterprise Security frameworks are responsible for programmatically associating this information?
Answer: B
Explanation:
The relationship shown in the question is produced through the interaction of the Threat Intelligence Framework and the Risk Framework . Threat intelligence provides known or suspected malicious indicators-such as IP addresses, domains, URLs, file hashes, or other observable objects-that can be matched against security telemetry.
When activity involving those indicators is detected, Enterprise Security can associate that activity with a risk object , such as a user or system, and accumulate risk through the Risk Framework. Rather than immediately treating every individual match as a standalone high-severity incident, risk-based analytics can combine multiple pieces of evidence and build a more meaningful representation of potentially compromised entities.
The screenshot on page 2 displays the Risk Events context, reinforcing that the entities are being represented in terms of accumulated security risk rather than merely listed as asset inventory records. The Assets and Identities framework can enrich entities with contextual information, but the central association described by the question is threat-intelligence evidence being transformed into risk against relevant objects.
This relationship supports higher-confidence detection by combining indicator evidence with entity-centric risk aggregation.
Study Guide topics: Threat Intelligence Framework, Risk Framework, risk objects, threat matching, risk events, risk-based analytics.
NEW QUESTION # 62
Engineers are commonly asked to turn data sources like EDR alerts into risk events. Doing so requires a dynamic mapping of the signatures in the rule to MITRE ATT&CK. Which of the following fields could be used to dynamically set the MITRE ATT&CK technique ID for the EDR alerts?
Answer: A
Explanation:
Risk-based alerting expects MITRE ATT&CK mappings to be provided through the annotations namespace. The correct dynamic field for specifying the ATT&CK technique ID is annotations.mitre_attack.mitre_technique_id, which Splunk uses when generating risk events.
NEW QUESTION # 63
A company's Splunk setup processes logs from multiple sources with inconsistent field naming conventions.
Howshould the engineer ensure uniformity across data for better analysis?
Answer: B
Explanation:
Why Use CIM for Field Normalization?
When processing logs from multiple sources with inconsistent field names, the best way to ensure uniformity is to use Splunk's Common Information Model (CIM).
#Key Benefits of CIM for Normalization:
Ensures that different field names (e.g., src_ip, ip_src, source_address) are mapped to a common schema.
Allows security teams to run a single search query across multiple sources without manual mapping.
Enables correlation searches in Splunk Enterprise Security (ES) for better threat detection.
Example Scenario in a SOC:
#Problem: The SOC team needs to correlate firewall logs, cloud logs, and endpoint logs for failed logins.
#Without CIM: Each log source uses a different field name for failed logins, requiring multiple search queries.
#With CIM: All failed login events map to the same standardized field (e.g., action="failure"), allowing one unified search query.
Why Not the Other Options?
#A. Create field extraction rules at search time - Helps with parsing data but doesn't standardize field names across sources.#B. Use data model acceleration for real-time searches - Accelerates searches but doesn't fix inconsistent field naming.#D. Configure index-time data transformations - Changes fields at indexing but is less flexible than CIM's search-time normalization.
References & Learning Resources
#Splunk CIM for Normalization: https://docs.splunk.com/Documentation/CIM#Splunk ES CIM Field Mappings: https://splunkbase.splunk.com/app/263#Best Practices for Log Normalization: https://www.splunk.
com/en_us/blog/tips-and-tricks
NEW QUESTION # 64
What are the main steps of the Splunk data pipeline?(Choosethree)
Answer: B,C,D
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 # 65
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