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최신 Security Operations XSIAM-Engineer 무료샘플문제 (Q278-Q283):
질문 # 278
A large enterprise is migrating security logs from an on-premise SIEM to XSIAM. A critical subset of these logs, originating from custom applications, uses a highly irregular, multiline log format where a single logical event spans several lines, with key information often on different lines. For instance, a 'transaction ID' might be on line 1, 'event type' on line 3, and 'result code' on line 5. Designing an XSIAM Data Flow parser for this scenario presents significant challenges. Which of the following strategies are crucial for effectively parsing and normalizing such unique, multiline, and irregular data into actionable XSIAM records?
- A. Leverage XSIAM's Machine Learning capabilities to automatically identify patterns and extract fields from the multiline logs without explicit parsing rules.
- B. Ingest the raw multiline logs into the Data Lake as-is, and rely solely on complex XQL queries with string manipulation functions like strcat() and substring() to extract information at query time.
- C. Utilize XSIAM's 'Multiline Log Parser' feature, defining a 'start pattern' regex to identify the beginning of an event and then using multiple parse_regex() or parse_kv() functions within a single Data Flow for each relevant line, correlating data using shared identifiers like a transaction ID.
- D. Implement an external log pre-processor (e.g., a custom Python script or Logstash) to aggregate multiline events into single JSON objects before forwarding them to XSIAM via a standard HTTP collector.
- E. Configure multiple independent Data Flow parsers, one for each line of the multiline event, and then use XQL join operations in the Data Lake to reconstruct the full event.
정답:C,D
설명:
This is a multiple-response question. Both B and C are viable strategies, depending on the specific context and complexity. Option B is a native XSIAM solution: XSIAM's Multiline Log Parser is specifically designed for such scenarios. It allows defining a start pattern to group related lines into a single logical event before subsequent parsing. Within that single event, multiple parse_regex() or parse_kv() operations can then extract fields from different lines, using a common identifier (like a transaction ID) for correlation within the same event. Option C is also a common and effective approach, especially if the multiline parsing logic is highly complex or requires custom logic not easily expressed in Data Flow. Pre-processing the logs externally ensures that XSIAM receives well-formed, single-event records, simplifying subsequent ingestion and analysis. Option A is inefficient and prone to errors due to the difficulty of reliably joining disparate event fragments. Option D is highly inefficient for large datasets and makes real-time analysis challenging. Option E (ML-based parsing) is generally for unstructured or semi-structured data, not for highly irregular but logically structured multiline events where explicit rules are needed.
질문 # 279
What is the purpose of using rolling tokens to manage Cortex XDR agents?
- A. To authorize agents to download and install content updates
D To temporarily disable the agents during maintenance windows - B. To perform administration on agents without requiring static credentials
- C. To periodically rotate encryption keys used for tenant communication
정답:B
설명:
Rolling tokens in Cortex XDR are used to perform administration on agents without relying on static credentials. This improves security by providing time-limited, automatically rotating tokens that maintain agent management access without exposing long-lived credentials.
질문 # 280
An XSIAM engineer is designing an automated incident response playbook for critical cloud workloads running on AWS. The playbook needs to ingest various AWS logs (CloudTrail, VPC Flow Logs, GuardDuty findings), trigger on specific high-severity alerts, and then execute remediation actions (e.g., quarantine EC2 instance, block malicious IP in Security Group, revoke IAM role). Which components and configurations are essential within XSIAM to enable this end-to-end automation, including data ingestion, alert correlation, and orchestrated response?
- A. Set up AWS CloudWatch to send all logs to a Lambda function, which then pushes the data directly to XSIAM's Ingestion API. Define simple alert rules within XSIAM based on keyword matches, and configure manual SOAR actions to be triggered by the SOC team.
- B. Configure AWS S3 buckets for log archiving, then use a scheduled XSIAM Data Collector to pull logs from S3. Create advanced correlation rules in XSIAM using XQL, and integrate with a third-party SOAR platform to execute remediation actions via API calls.
- C. Integrate AWS Security Hub with XSIAM to receive consolidated findings. Configure XSIAM to fomard these findings to a ticketing system, and rely on human operators to manually implement remediation steps.
- D. Utilize the native XSIAM AWS Data Connector to ingest logs from S3 buckets and CloudWatch Logs. Define XQL-based Correlation Rules for alert generation. Develop XSIAM Playbooks that leverage the AWS Actions app (e.g., 'Update Security Group', 'Stop Instance') to automate remediation directly within XSIAM.
- E. Deploy Cortex XDR agents on all AWS EC2 instances to collect endpoint telemetry. Use these alerts to manually trigger remediation scripts on the compromised instances via SSH.
정답:D
설명:
To achieve end-to-end automation for cloud incident response within XSIAM, leveraging its native capabilities is key. Option C is the most effective and integrated approach: 1. Ingestion: The native XSIAM AWS Data Connector is designed for efficient and reliable ingestion of various AWS logs (CloudTrail, VPC Flow Logs, GuardDuty, etc.) from their respective sources (S3, CloudWatch Logs). This is the primary and recommended method for AWS data onboarding. 2. Alert Correlation: XQL-based Correlation Rules are fundamental for creating sophisticated detections within XSIAM by correlating events across various data sources (e.g., CloudTrail showing an IAM role creation, VPC Flow Logs showing suspicious outbound traffic, and GuardDuty detecting anomalous activity). 3. Orchestrated Response: XSIAM Playbooks provide the automation engine. These playbooks can be triggered by the correlation alerts and leverage the AWS Actions app (or other relevant integrations) to perform direct remediation actions within AWS, such as updating security groups to block malicious IPs, stopping or isolating EC2 instances, or revoking compromised IAM roles. This keeps the entire workflow within XSIAM, ensuring seamless orchestration. Option A: Relies on external Lambda for ingestion and manual SOAR, which defeats XSIAM's automation purpose. Option B: Using scheduled S3 pulls introduces latency. Integrating with a third-party SOAR platform adds unnecessary complexity when XSIAM has native playbook capabilities. Option D: Cortex XDR agents are for endpoint telemetry, not for ingesting cloud service logs, and manual SSH remediation is not automation. Option E: Integrating with Security Hub is good for findings consolidation, but forwarding to a ticketing system for manual remediation falls short of the desired automation.
질문 # 281
Consider an XSIAM deployment aiming for high availability and disaster recovery across multiple geographical regions. The plan involves integrating data from a highly distributed environment including on-premise networks, AWS, Azure, and GCP. When evaluating the network connectivity requirements for XSIAM Data Collectors and ensuring optimal data ingestion, which factors are most critical?
- A. Ensuring sufficient bandwidth and low latency between on-premise Data Collectors and the XSIAM cloud, potentially via dedicated VPN tunnels or SD-WAN.
- B. Implementing private network connectivity (e.g., AWS Direct Connect, Azure ExpressRoute, GCP Cloud Interconnect) from each cloud environment to the XSIAM cloud region(s).
- C. Configuring network ACLs and security groups to allow outbound HTTPS (port 443) traffic only from Data Collectors to XSIAM ingestion endpoints.
- D. Deploying Data Collectors only in the primary XSIAM cloud region to centralize data flow.
- E. Utilizing public internet connectivity exclusively to simplify network architecture and reduce costs.
정답:A,B,C
설명:
For a highly available and distributed XSIAM deployment, options B, C, and D are critical. Option B ensures secure and high-performance private connectivity from cloud environments. Option C addresses bandwidth and latency for on-premise data. Option D specifies then ecessary security posture for Data Collector egress. Option A is generally not recommended for sensitive security data due to security and performance concerns. Option E would create a single point of failure and negate distributed data collection benefits.
질문 # 282
An organization relies heavily on a complex, multi-cloud environment (AWS, Azure, GCP) and uses a centralized cloud security posture management (CSPM) solution that reports configuration drift and compliance violations. They want to integrate the CSPM alerts into XSIAM to automatically create incidents, enrich them with cloud asset details (e.g., resource tags, associated VPCs), and trigger automated remediation playbooks. The CSPM solution exports alerts in a highly nested JSON format via an API, and asset details are available through respective cloud provider APIs. Which XSIAM integration strategy offers the most resilient, scalable, and intelligent automation for this multi-cloud scenario, and what challenges might arise with data normalization?
- A. Export CSPM alerts as CSV files to an S3 bucket in AWS. An XSIAM Data Collector pulls these CSVs. Automated remediation is handled by the CSPM solution directly, not XSIAM. Challenges: Latency in CSV export, limited enrichment, no XSIAM-driven remediation.
- B. Manually create XSIAM incidents based on high-priority CSPM alerts. Enrichment and remediation are performed manually by security analysts. Challenges: Not scalable, high operational overhead, prone to human error.
- C. Configure the CSPM solution to send email alerts to XSIAM's email ingestion service. XSIAM playbooks parse the email content to create incidents and then make separate API calls to each cloud provider to fetch asset details for enrichment. Challenges: Email parsing is unreliable, rate limits on cloud APIs.
- D. Develop a custom XSIAM content pack that includes a Data Collector integration to periodically pull alerts from the CSPM API. The content pack would define a custom data model to map the nested JSON into XSIAM fields. An XSIAM Playbook, triggered by these incidents, would dynamically call the relevant cloud provider's API (based on cloud type in the incident) to fetch additional asset details using XSIAM's native cloud connectors (if available) or 'Call API' tasks, and then trigger automated remediation actions. Challenges: Mapping complex nested JSON to a flat XSIAM data model, consistent data normalization across different cloud provider asset details (e.g., 'resource_id' vs 'instanceld').
- E. The CSPM solution sends all alerts to a common SIEM. The SIEM then processes, normalizes, and enriches the data, finally fomarding it to XSIAM via CEE XSIAM then triggers playbooks. Challenges: Adds an expensive and complex intermediate SIEM, potential for data loss or delay, limited native XSIAM control over enrichment.
정답:D
설명:
For a complex multi-cloud environment with a CSPM solution delivering nested JSON alerts and requiring dynamic enrichment/remediation, developing a custom XSIAM content pack is the most resilient, scalable, and intelligent approach. This allows for precise control over data ingestion from the CSPM API, enabling proper mapping of the highly nested JSON into XSIAM's structured data model. An XSIAM Playbook, intelligently triggered by these incidents, can then dynamically identify the cloud provider and use XSIAM's native cloud connectors (if supported) or 'Call API' tasks to fetch highly specific asset details from AWS, Azure, or GCP. This enriched data can then be used to inform and trigger automated remediation. The primary challenge, and a critical consideration, is data normalization: ensuring that similar concepts (e.g., resource identifiers, network configurations, tags) from different cloud providers are consistently mapped and represented within XSIAM to enable effective correlation and playbook execution without needing complex conditional logic for each cloud's unique field names. This custom content pack approach provides the flexibility to handle such complexity.
질문 # 283
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