SPLK-5002합격보장가능인증덤프 - SPLK-5002높은통과율시험공부자료

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Splunk SPLK-5002 시험요강:

주제소개
주제 1
  • Detection Engineering: This section evaluates the expertise of Threat Hunters and SOC Engineers in developing and refining security detections. Topics include creating and tuning correlation searches, integrating contextual data into detections, applying risk-based modifiers, generating actionable Notable Events, and managing the lifecycle of detection rules to adapt to evolving threats.
주제 2
  • Automation and Efficiency: This section assesses Automation Engineers and SOAR Specialists in streamlining security operations. It covers developing automation for SOPs, optimizing case management workflows, utilizing REST APIs, designing SOAR playbooks for response automation, and evaluating integrations between Splunk Enterprise Security and SOAR tools.
주제 3
  • Auditing and Reporting on Security Programs: This section tests Auditors and Security Architects on validating and communicating program effectiveness. It includes designing security metrics, generating compliance reports, and building dashboards to visualize program performance and vulnerabilities for stakeholders.
주제 4
  • Data Engineering: This section of the exam measures the skills of Security Analysts and Cybersecurity Engineers and covers foundational data management tasks. It includes performing data review and analysis, creating and maintaining efficient data indexing, and applying Splunk methods for data normalization to ensure structured and usable datasets for security operations.
주제 5
  • Building Effective Security Processes and Programs: This section targets Security Program Managers and Compliance Officers, focusing on operationalizing security workflows. It involves researching and integrating threat intelligence, applying risk and detection prioritization methodologies, and developing documentation or standard operating procedures (SOPs) to maintain robust security practices.

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최신 Cybersecurity Defense Analyst SPLK-5002 무료샘플문제 (Q71-Q76):

질문 # 71
During an incident, a correlation search generates several notable events related to failed logins. The engineer notices the events are from test accounts.
Whatshould be done to address this?

정답：B

설명：
When a correlation search in Splunk Enterprise Security (ES) generates excessive notable events due to test accounts, the best approach is to filter out test accounts while keeping legitimate detections active.
#1. Apply Filtering to Exclude Test Accounts (B)
Modifies the correlation search to exclude known test accounts.
Reduces false positives while keeping real threats visible.
Example:
Update the search to exclude test accounts:
index=auth_logs NOT user IN ("test_user1", "test_user2")
#Incorrect Answers:
A: Disable the correlation search for test accounts # This removes visibility into all failed logins, including those that may indicate real threats.
C: Lower the search threshold for failed logins # Would increase false positives, making it harder for SOC teams to focus on real attacks.
D: Suppress all notable events temporarily # Suppression hides all alerts, potentially missing real security incidents.
#Additional Resources:
Splunk ES: Managing Correlation Searches
Reducing False Positives in SIEM


질문 # 72
When creating detections, which of the following sequences would result in the most performant SPL query?

정답：A

설명：
The most performant SPL design is to define the base query, minimize the data set as early as possible, combine or summarize the remaining data, perform calculations, and format the final output last.
The critical optimization principle is early reduction of search cardinality. Filtering unnecessary events and fields before expensive aggregation or calculation means downstream commands operate on substantially less data. Once the search has constrained the relevant events, aggregation commands such as stats, tstats, or equivalent summarization reduce the event stream further. Calculations with eval or related functions should then operate on this smaller result set, and display-oriented operations such as table, rename, or final formatting should be performed only after analytical processing is complete.
Option A performs aggregation before minimizing the dataset, potentially requiring unnecessary events to participate in expensive operations. Options C and D perform formatting too early, which does not improve detection execution and can complicate or increase downstream processing.
The supplied study material also emphasizes efficient indexed/accelerated searching such as tstats instead of unnecessarily broad raw-event processing, reinforcing the same performance principle.
Study Guide topics: performant SPL, early filtering, aggregation, stats, tstats, search optimization, detection engineering efficiency.


질문 # 73
Which Splunk configuration ensures events are parsed and indexed only once for optimal storage?

정답：C

설명：
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.


질문 # 74
When building a metrics dashboard for the SOC manager, which metric would represent how long it takes to fully complete an investigation?

정답：C

설명：
MTTR (Mean Time to Resolution/Recovery/Respond) measures how long it takes to fully complete an investigation or resolve an incident. This is the key metric for tracking investigation completion time in SOC performance dashboards.


질문 # 75
Which REST API method is used to retrieve data from a Splunk index?

정답：A

설명：
The GET method in the Splunk REST API is used to retrieve data from a Splunk index. It allows users and automated scripts to fetch logs, alerts, or query results programmatically.
Key Points About GET in Splunk API:
Used for searching and retrieving logs from indexes.
Can be used to get search results, job status, and Splunk configuration details.
Common API endpoints include:
/services/search/jobs/{search_id}/results- Retrieves results of a completed search.
/services/search/jobs/export- Exports search results in real-time.


질문 # 76
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