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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security Automation (SOAR) | 30% | - Playbook design and automation workflows - Incident response automation and orchestration |
| Topic 2: Data Engineering | 10% | - Data ingestion and onboarding - Indexing performance and management - Data parsing, normalization, and CIM alignment |
| Topic 3: Detection Engineering | 40% | - Creation and tuning of detections (Correlation Searches) - Detection enrichment with context and risk-based alerting - Notable event generation and lifecycle management |
| Topic 4: Security Operations and Program Development | 20% | - SOC process design and operational workflows - Threat intelligence integration |
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NEW QUESTION # 52
Consider the following series of events:
4:00 GMT Detection runs for interval 3:30-4:00
4:30 GMT Detection runs for interval 4:00-4:30
4:35 GMT Event 1 occurs on an endpoint
4:45 GMT Event 1 is indexed
5:00 GMT Detection runs for interval 4:30-5:00
5:05 GMT Event 1 finding is added to ES with timestamp 4:35
5:24 GMT Event 2 occurs on an endpoint
5:30 GMT Detection runs for interval 5:00-5:30
5:35 GMT Event 2 is indexed
6:00 GMT Detection runs for interval 5:30-6:00
What is the problem with the detection schedule chosen and how can it be solved?
Answer: B
Explanation:
The issue is ingestion latency . Event 2 occurs at 5:24 but is not indexed until 5:35. The 5:30 scheduled search covers event time 5:00-5:30, but the event is unavailable when that search executes. By the next execution at 6:00, the search window has advanced to 5:30-6:00, so an event timestamped 5:24 can fall outside the new window even though it is now searchable.
The solution is to increase the detection ' s lookback window sufficiently to accommodate expected indexing delay. Operationally, scheduled detections frequently use overlapping windows-for example, executing every 30 minutes while looking back farther than 30 minutes-to prevent late-arriving events from being missed. Appropriate deduplication or throttling can then address repeated matches caused by overlap.
Decreasing the time window would make the latency problem worse. The scenario also does not indicate that an excessively large window is causing duplicate alerts; it demonstrates a late-arriving event that misses the search interval.
Study Guide topics: detection scheduling, ingestion latency, event time versus index time, lookback windows, late-arriving telemetry.
NEW QUESTION # 53
What is the main benefit of automating case management workflows in Splunk?
Answer: D
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 # 54
A compliance audit reveals gaps in the tracking of privileged account activities.
Howcan the team address this issue?
Answer: B
Explanation:
Privileged accounts pose ahigh security risk, and tracking their activity iscritical for compliance(e.g.,PCI DSS, NIST, ISO 27001, SOC 2).
#1. Automate Report Generation for Privileged Accounts (A)
Ensurescontinuous monitoringofadmin/root accounts.
Helpsdetect misuse or unauthorized access.
Example:
Splunk Enterprise Security (ES)can generate scheduled reports on:
Failed login attempts by privileged users.
Actions performed using admin credentials.
#Incorrect Answers:
B: Use summary indexes to delete old data# Summary indexes improve performance butdo not help track privileged accounts.
C: Focus only on low-priority account activity# Privileged accountsshould always be high-priority.
D: Exclude privileged accounts from reporting# This wouldviolate compliance requirements.
#Additional Resources:
Splunk Security Monitoring for Privileged Accounts
NIST Access Control Guide
NEW QUESTION # 55
What field is used by default to direct data into CIM data model datasets?
Answer: B
Explanation:
The default mechanism represented by the question is the tag field. CIM data-model datasets use constraints that commonly depend on tags applied through event types and other Splunk knowledge objects to determine whether events belong to a particular semantic dataset.
For example, events associated with a particular CIM category can be tagged so that the corresponding dataset constraint recognizes them. The underlying vendor data may originate from many different sourcetypes, but the CIM abstraction allows all correctly classified and normalized records to participate in a common data model.
A sourcetype remains extremely important because it identifies the format and parsing context of indexed data, but CIM is intentionally designed to support multiple vendors and sourcetypes within the same semantic dataset. Consequently, sourcetype alone is not the default abstraction requested here. source identifies the origin of data and similarly does not define CIM dataset membership. dataset is not the event field used by default for this routing behavior.
Correct tagging must be combined with proper CIM field normalization for downstream searches to produce meaningful standardized results.
Study Guide topics: CIM tags, dataset constraints, event types, CIM normalization, data-model membership, sourcetypes.
NEW QUESTION # 56
Which of the following identifies elements of the Detection Development Lifecyle (DDLC)?
Answer: B
Explanation:
The Detection Development Lifecycle (DDLC) includes the stages Design, Develop, Deploy, Monitor, and Maintain. This structured process ensures detections are thoughtfully built, effectively deployed, and continuously refined for accuracy and relevance.
NEW QUESTION # 57
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