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最新的 Security Operations XSIAM-Engineer 免費考試真題:

1. As part of XSIAM's planning phase, an organization is assessing its existing data governance policies. They have strict data retention periods for different log types (e.g., 90 days for network flows, 1 year for endpoint activity, 7 years for audit logs). Additionally, certain data types are subject to anonymization requirements before being stored in a cloud platform. How can these requirements be reconciled with XSIAM's unified data lake architecture, and what XSIAM features or best practices should be leveraged?

A) The organization should continue using their on-premise SIEM for long-term retention and anonymization, and only forward real-time, un-anonymized data to XSIAM for immediate threat detection.
B) XSIAM's unified data lake has a fixed, unconfigurable retention policy, so the organization must adjust its internal policies to match XSIAM. Anonymization requires manual pre-processing before ingestion.
C) XSIAM's architecture is not suitable for organizations with complex data retention or anonymization requirements; they should consider an on-premise solution.
D) All data ingested into XSIAM is automatically anonymized and retained for 7 years by default, simplifying compliance. No further configuration is needed.
E) XSIAM allows for configurable data retention policies based on data source or type, enabling different retention periods within the platform. For anonymization, XSIAM's data transformation capabilities (e.g., during ingestion via Data Collectors or through specific mapping rules) can be used to mask sensitive fields before storage. Data governance should include proper role-based access control (RBAC) within XSIAM.

2. An organization is migrating from a traditional SIEM to Palo Alto Networks XSIAM. They have a large collection of custom correlation rules written in Splunk's SPL. A key objective is to translate these rules to XSIAM's Alert Query Language (AQL) to maintain existing detection capabilities. During the planning and resource evaluation, what is the most significant technical challenge to anticipate, and which XSIAM feature/resource is most critical for addressing it efficiently?

A) The absence of a graphical rule builder in XSIAM, forcing all rule creation to be done via command-line AQL.
B) XSIAM's inability to ingest historical Splunk logs, necessitating a fresh start for all detection logic.
C) The lack of direct Splunk SPL to XSIAM AQL automated conversion tools; requiring manual translation efforts and a strong understanding of both languages' syntax and data models.
D) Insufficient storage capacity in Cortex Data Lake (CDL) to accommodate the translated rules, which are typically much larger in AQL than SPL.
E) The XSIAM Analytics Engine (XAE) being incompatible with custom AQL rules, limiting detection to Palo Alto Networks' pre-defined content.

3. A Security Operations Center (SOC) using Palo Alto Networks XSIAM has identified a significant number of false positives from a recently deployed indicator rule designed to detect suspicious PowerShell activity. The rule currently triggers on any PowerShell execution that includes a base64 encoded string. The SOC wants to optimize this rule to reduce false positives while maintaining detection efficacy. Which of the following approaches is MOST effective for content optimization in this scenario?

A) Create a new 'allow list' rule that explicitly permits all legitimate PowerShell activity, and ensure it has a higher precedence than the detection rule.
B) Decrease the severity of the existing indicator rule to 'Low' so it generates fewer high-priority alerts.
C) Refine the indicator rule's query to include additional contextual filters, such as process parent-child relationships (e.g., PowerShell spawned by non-standard processes) or specific base64 decode lengths/patterns known to be malicious, using XQL.
D) Increase the time window for the indicator rule's correlation logic to reduce the frequency of triggers.
E) Disable the existing indicator rule entirely and rely on other XSIAM out-of-the-box detections.

4. A newly deployed XSIAM indicator rule designed to detect 'Ransomware Activity' is generating an unmanageable number of alerts. The rule broadly looks for 'File Write' events where matches common ransomware extensions (e.g., '.locked', .crypt' , .encrypt' ). Analysis reveals legitimate file encryption tools and development activities are the primary false positive sources. You need to significantly reduce false positives while ensuring high-fidelity detection of actual ransomware. Which combination of XSIAM content optimization techniques would be most effective?

A) Increase the number of file extensions in the rule to include even more ransomware variants, and set the severity to 'High'.
B) Leverage XSIAM's 'Machine Learning' capabilities to identify anomalous file encryption patterns, potentially creating a separate behavioral rule or using built-in XDR analytics for ransomware.
C) Modify the XQL to correlate File Writes events with suspicious 'Process Creation' events (e.g., 'cmd.exe' executing 'vssadmin delete shadows'), or 'Network Connection' attempts to known C2 infrastructure, within a short time window and by the same user/host.
D) Add a filter to only trigger if the 'file_size' is above IGB, assuming ransomware encrypts large files.
E) Implement an exclusion for 'process_name' of known legitimate encryption applications (e.g., 'WinZip.exe', 'GnuPG.exe') from the rule.

5. Consider an XSIAM deployment where the customer wants to integrate an internal proxy server for all outbound XSIAM Data Collector communications to the XSIAM Data Lake and other cloud services. The proxy requires NTLM authentication and performs deep packet inspection (DPI). What are the critical communication challenges and configuration considerations for this scenario, and how might they impact data ingestion and XSIAM functionality?

A) Only HTTP proxies are supported, and NTLM is an HTTP-specific authentication, making it compatible. DPI is irrelevant as XSIAM encrypts all traffic at the application layer.
B) XSIAM Data Collectors fully support NTLM proxy authentication natively, and DPI will not interfere with encrypted TLS traffic, simplifying deployment.
C) Data Collectors will automatically detect and configure themselves to use the NTLM proxy, and DPI will only inspect unencrypted metadata, not payload.
D) The proxy server must be configured to bypass all XSIAM traffic entirely, negating the purpose of the proxy for XSIAM communications.
E) NTLM authentication is generally not supported directly by XSIAM Data Collectors for outbound proxy. DPI on encrypted TLS traffic will break the mutual trust established by certificates, leading to communication failures unless the proxy performs SSL/TLS interception and the XSIAM Data Collectors are configured to trust the proxy's root certificate.

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