CCSE-204前提条件、CCSE-204日本語版問題集

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CrowdStrike CCSE-204 Exam Syllabus Topics:

Section	Weight	Objectives
Dashboards and Reporting	20%	- Visualization Techniques 1. Report scheduling 2. Dashboard creation
Log Management and Data Collection	25%	- Data Normalization 1. Parsing rules 2. Common Information Model (CIM) - Data Sources and Connectors 1. Cloud-native log sources 2. Third-party integrations
Administration and Maintenance	25%	- System Health Monitoring 1. Performance tuning 2. Storage management - Access Control 1. Role-based access 2. Authentication methods
Search and Investigation	30%	- Search Processing Language (SPL) 1. Statistical functions 2. Basic search commands - Incident Investigation 1. Timeline analysis 2. Evidence gathering

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CrowdStrike Certified SIEM Engineer 認定 CCSE-204 試験問題 (Q10-Q15):

質問 # 10
You are creating an AI-generated parser to process and normalize log data from various sources.
How would you ensure the parser accurately interprets and categorizes the log data?

A. Ensure the parser has a minimum of 100 lines
B. Write the parser in a high-level programming language (Python or Java)
C. Create a set of log examples to match log patterns from different sources

正解：C

解説：
The correct answer is B . CrowdStrike states that AI-generated parsers are built from sample log records .
Falcon Next-Gen SIEM analyzes those samples to learn the logs' structure and content, so providing representative examples is the documented way to help the parser interpret and categorize data correctly.
Options A and C are not supported by CrowdStrike documentation. There is no requirement for a minimum parser length, and Next-Gen SIEM parsers are not written as Python or Java programs; CrowdStrike's parser template shows a parser schema and script structure specific to Next-Gen SIEM.

質問 # 11
You are creating an AI-generated parser to process and normalize log data from various sources.
How would you ensure the parser accurately interprets and categorizes the log data?

A. Ensure the parser has a minimum of 100 lines
B. Write the parser in a high-level programming language (Python or Java)
C. Create a set of log examples to match log patterns from different sources

正解：C

解説：
Providing representative log examples allows an AI-generated parser to learn the structure and patterns of different log formats, ensuring accurate field extraction and normalization across diverse data sources.

質問 # 12
Which SIEM capability allows analysts to enrich Falcon alerts with external threat intelligence feeds to improve investigation context?

A. Enrichment
B. Parsing
C. Compression
D. Aggregation

正解：A

解説：
Enrichment adds context such as known malicious IPs or domains.

質問 # 13
You want a consistent view of events from various data sources.
Which ECS field type should you normalize?

A. Extended Fields
B. Detection Fields
C. Core Fields
D. Base Fields

正解：C

解説：
Normalizing events to Core Fields in the Elastic Common Schema (ECS) provides a consistent structure across different data sources, enabling reliable search, correlation, and detection in Next-Gen SIEM.

質問 # 14
Following the principle of least privilege, which is the appropriate role to grant a Falcon Next-Gen SIEM user the permissions to read case data and write XDR data while denying the permission to write case templates?

A. NG SIEM Analyst
B. NG SIEM Analyst - Read Only
C. NGSIEM Administrator
D. NG SIEM Security Lead

正解：A

解説：
The best answer is C. NG SIEM Analyst .
I need to be careful here: I did not find a public CrowdStrike permissions matrix that explicitly lists this exact combination of rights by role. So this answer is the best-supported least-privilege inference , not one I can claim is directly documented 100%.
Why C is the strongest choice:
* NG SIEM Analyst - Read Only would not fit because the question requires write XDR data permissions.
* NGSIEM Administrator and NG SIEM Security Lead are broader roles and would not satisfy least privilege if a narrower analyst role can do the job.
* That leaves NG SIEM Analyst as the most plausible least-privilege built-in role for reading case data and writing XDR data while not granting broader administrative capabilities. CrowdStrike's Next-Gen SIEM materials describe the platform as combining centralized case management and XDR workflows, but the public pages I found do not expose the exact internal role matrix.

質問 # 15
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CCSE-204日本語版問題集: https://www.jpntest.com/shiken/CCSE-204-mondaishu