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NEW QUESTION # 81
What methods improve the efficiency of Splunk's automation capabilities? (Choose three)
Answer: A,D,E
Explanation:
How to Improve Splunk's Automation Efficiency?
Splunk's automation capabilities rely on efficient data ingestion, optimized searches, and automated response workflows. The following methods help improve Splunk's automation:
#1. Using Modular Inputs (Answer A)
Modular inputs allow Splunk to ingest third-party data efficiently (e.g., APIs, cloud services, or security tools).
Benefit: Improves automation by enabling real-time data collection for security workflows.
Example: Using a modular input to ingest threat intelligence feeds and trigger automatic responses.
#2. Optimizing Correlation Search Queries (Answer B)
Well-optimized correlation searches reduce query time and false positives.
Benefit: Faster detections # Triggers automated actions in SOAR with minimal delay.
Example: Usingtstatsinstead of raw searches for efficient event detection.
#3. Employing Prebuilt SOAR Playbooks (Answer E)
SOAR playbooks automate security responses based on predefined workflows.
Benefit: Reduces manual effort in phishing response, malware containment, etc.
Example: Automating phishing email analysis using a SOAR playbook that extracts attachments, checks URLs, and blocks malicious senders.
Why Not the Other Options?
#C. Leveraging saved search acceleration - Helps with dashboard performance, but doesn't directly improve automation.#D. Implementing low-latency indexing - Reduces indexing lag but is not a core automation feature.
References & Learning Resources
#Splunk SOAR Automation Guide: https://docs.splunk.com/Documentation/SOAR#Optimizing Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation/ES#Prebuilt SOAR Playbooks for Security Automation: https://splunkbase.splunk.com
NEW QUESTION # 82
When creating a case in Splunk SOAR, which action should be taken to correlate various findings (risk notables) to ensure all are actioned?
Answer: C
Explanation:
When creating a case in Splunk SOAR, correlation is achieved by searching Splunk Enterprise Security for all related events based on key fields in a risk notable, then deciding how to process and merge those events into the investigation. This ensures that all relevant risk notables are actioned together for a complete response.
NEW QUESTION # 83
When creating a detection that searches user activity across CIM-compliant data, which CIM field should be reviewed to ensure that data is aggregated appropriately?
Answer: D
Explanation:
The correct field is user , because Splunk ' s Common Information Model provides normalized fields that allow heterogeneous security data sources to be queried consistently. Authentication, endpoint, identity, operating-system, and application logs frequently use different native names for the same concept-for example, username, account_name, UserName, or src_user. CIM mappings normalize the appropriate value into the canonical user field.
This becomes particularly important when a detection performs aggregation such as:
| stats count by user
If contributing sources fail to populate user consistently, activity belonging to the same account can be fragmented, omitted, or incorrectly represented in the detection results. action identifies the result or nature of activity rather than the account responsible for it. user_id may occur in individual schemas but is not the general CIM field being tested here. identity represents a different conceptual object and is not the standard aggregation field for this use case.
The question appears on page 1 of the supplied certification material.
Study Guide topics: Common Information Model, CIM normalization, normalized fields, data-model consistency, user attribution, detection data preparation.
NEW QUESTION # 84
What framework in Enterprise Security allows engineers to build detections using known malicious IOCs, comparing them to event logs to find suspicious behavior?
Answer: C
Explanation:
The Threat Intelligence Framework provides the Enterprise Security capability for managing known indicators of compromise and correlating those indicators with observed event data. It is therefore the framework used when engineers want to compare malicious IOCs against telemetry to identify suspicious activity.
Indicators can represent objects such as IP addresses, domains, URLs, file hashes, certificates, or other observable threat artifacts. Enterprise Security processes and normalizes threat intelligence so that matching logic can compare those intelligence objects against compatible fields contained in security events.
For example, a known malicious domain may be compared against DNS or web telemetry, while a malicious IP address may be compared against network connections. A match can subsequently contribute to a finding, risk event, enrichment workflow, or additional investigation depending on the implemented detection strategy.
The Assets & Identities functionality concerns organizational context around entities rather than IOC matching. Incident management handles investigation and response workflows. OSINT describes a category of intelligence collection but is not the Enterprise Security framework requested.
Study Guide topics: Threat Intelligence Framework; IOCs; threat matching; threat-intelligence normalization; event correlation; indicator-based detection.
NEW QUESTION # 85
When creating detections, which of the following sequences would result in the most performant SPL query?
Answer: D
Explanation:
The most performant SPL query sequence is:
Define base query → Minimize data → Combine/Summarize data → Execute calculations → Format the data.
Minimizing the data early (using filters, time constraints, and field limitations) reduces the dataset before expensive operations like summarization or calculations, resulting in optimal performance.
NEW QUESTION # 86
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