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NEW QUESTION # 264
An organization relies heavily on custom correlation rules within XSIAM. After a mandatory XSIAM content update, several critical custom rules are no longer triggering, despite relevant raw events being ingested. The XSIAM console shows the custom rules as 'Enabled' and 'Validated'. What is the most sophisticated and often overlooked reason for this behavior, and what's the recommended diagnostic approach?
Answer: E
Explanation:
This is a very common and difficult-to-diagnose issue after content updates. While the custom rules may be syntactically valid ('Validated'), the underlying schema of events ingested into XSIAM can change with content updates (e.g., a field name changes from event.action' to 'action_name' , or a new normalized field is introduced). If a custom rule relies on a field that no longer exists or has changed its name, it won't find the necessary data to trigger, even if the raw events are present. Using XQL to inspect the actual schema of recent relevant events is the most effective way to identify such discrepancies and then modify the custom rule to match the new schema.
NEW QUESTION # 265
In which two locations can correlation rules be monitored for errors? (Choose two.)
Answer: B,C
Explanation:
Correlation rule errors can be tracked in XDR Collector audit logs (type = Rules, subtype = Error) and by querying the correlations_auditing dataset through XQL. These provide visibility into execution issues and failures for correlation rules.
NEW QUESTION # 266
A Security Operations Center (SOC) using Palo Alto Networks XSIAM receives a new threat intelligence feed in a proprietary, nested JSON format that includes threat actor profiles, TTPs (Tactics, Techniques, and Procedures), and IOCs (Indicators of Compromise). This feed is critical for proactive threat hunting. Which of the following XSIAM capabilities and configurations are essential to effectively ingest and optimize this unique data for analytics and correlation, considering the need for granular extraction of nested fields and normalization?
Answer: B
Explanation:
Option B is the most appropriate and robust solution. XSIAM's Data Flow language provides powerful capabilities for parsing complex, unique data formats like nested JSON. Functions like allow precise extraction of specific fields, while flatten( ) can handle arrays of objects. Defining a comprehensive schema in the Data Lake is crucial for normalization, ensuring consistency and usability of the extracted data for analytics, correlation, and threat hunting. This approach allows for granular control over data ingestion and optimization, which is vital for a proprietary threat intelligence feed. Option A is insufficient for granular, nested data. Option C introduces unnecessary complexity and potential data loss. Option D offloads parsing to query time, which is inefficient for large datasets and complex structures. Option E adds an external dependency and might not be as flexible as XSIAM's native parsing capabilities.
NEW QUESTION # 267
An XSIAM administrator is tasked with deploying a new XDR Agent version (7.5.0) to a highly sensitive environment with strict change control. They want to ensure that the new agent version does not introduce any new network connections or unexpected outbound traffic beyond the documented ingestion FQDNs. What is the most effective strategy to validate this, considering the update process and the need for thorough testing?
Answer: C
Explanation:
While consulting release notes (B) is a good first step, and a dry run (A) is beneficial, the most effective and thorough method for validating no new network connections in a highly sensitive environment is to deploy in a controlled, isolated test environment and perform deep packet inspection (C). A full PCAP will capture all outbound connections initiated by the agent, allowing for granular analysis against documented FQDNs. Firewall logs (A) might miss connections to permitted but previously unobserved FQDNs or temporary connections. Vulnerability scanning (D) is about open ports, not necessarily outbound connection behavior. Trusting the vendor (E) is insufficient for high-security environments.
NEW QUESTION # 268
Your organization uses XSIAM and has a critical requirement to monitor for 'Privilege Escalation' attempts within Linux environments, specifically looking for users attempting to execute commands with after a failed authentication attempt (indicating a brute-force or guessing attempt). The ASM rule should correlate 'xdr and 'xdr_process events' within a short time window. Which of the following XQL queries most accurately captures this scenario?
Answer: B
Explanation:
Option B is the most accurate and effective. It first filters for failed authentication attempts ('success = false') specifically on Linux devices. The crucial part is the operator. This allows correlating events across different datasets Cxdr_authentication_logS and 'xdr_process_eventS) that share common fields (username, device ID) within a specified short time window (1 minute). This precisely identifies the scenario: a failed login attempt followed quickly by a 'sudo' command by the same user on the same device. Option A lacks the crucial time-window correlation. Option C assumes 'sudo' command line will contain 'auth_error', which is not typical. Option D only identifies failed logins, not the subsequent 'sudo' attempt. Option E looks for successful 'sudo' and misses the failed authentication precursor.
NEW QUESTION # 269
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