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NEW QUESTION # 56
An XSIAM engineer needs to create an indicator rule that identifies attempts to disable security products. Specifically, the rule should look for command-line executions that attempt to stop or delete services related to Endpoint Detection and Response (EDR) agents or antivirus software, using common Windows commands like 'sc' or 'taskkill' combined with service names or process names. The challenge is to make this rule resilient to obfuscation and common legitimate administrative tasks. Which of the following XQL patterns best addresses this requirement for a high-fidelity indicator rule?
Answer: B
Explanation:
Option D is the most robust and high-fidelity choice. It correctly identifies the common commands ('sc stop', 'sc delete' , 'taskkill If /im') used for disabling services/processes. Crucially, it uses 'contains_any' with common substrings of security product names, making it resilient to variations. The 'not (user_name = 'SYSTEM' and parent_process_name = 'svchost.exe')' clause is a critical refinement to reduce false positives by excluding legitimate system-level service management activities, which often involve svchost.exe running as SYSTEM. Option A is too broad. Option B is too specific to a single service name. Option C's user_name exclusion is good but 'contains' for multiple strings is less efficient than 'contains_any'. Option E is too broad and prone to false positives.
NEW QUESTION # 57
An engineer is conducting a threat actor emulated test to determine which Cortex XDR module would provide protection or alert on a real-world attack. The first test was prevented.
Which action must the engineer take to enable continued testing?
Answer: A
Explanation:
To allow continued testing after the first emulated attack was blocked, the engineer must add an indicator exclusion. This bypasses enforcement for the specific test artifact, enabling repeated execution of the scenario to validate which Cortex XDR module detects or prevents the activity.
NEW QUESTION # 58
A critical zero-day vulnerability has been disclosed, and the XSIAM team needs to rapidly deploy a new detection rule. Due to the high potential impact, all alerts generated by this rule must immediately be prioritized and assigned the highest possible score, regardless of other contextual factors. Which XSIAM scoring rule configuration element is explicitly designed to achieve this immediate, overriding effect?
Answer: D
Explanation:
Option B is the correct approach. In XSIAM, the 'Set Total Score' action in a scoring rule allows you to explicitly override any previous scoring calculations and set a specific final score. By setting this to the maximum possible score (e.g., 100) and ensuring this scoring rule has a high evaluation 'Order', it guarantees that alerts from the new zero-day rule are immediately prioritized with the highest possible criticality, overriding any other conflicting scoring logic. Options A and C modify scores but don't guarantee an absolute override. Option D only affects the base score from the detection rule, which can still be modified by scoring rules. Option E is impractical and unnecessary.
NEW QUESTION # 59
A company is integrating a custom-developed application that produces logs in a proprietary JSON format. They need these logs ingested into Cortex XSIAM via a Broker VM. The JSON structure is complex and includes nested objects and arrays. To ensure proper parsing and normalization of these logs within XSIAM, what specific configurations are required on the Broker VM, and what considerations are paramount for the log format itself?
Answer: A
Explanation:
For custom JSON ingestion, the Broker VM's Universal Data Collector can be configured with an 'HTTP Listener' (C), providing a flexible endpoint for applications to send data. Crucially, because the JSON is proprietary, automatic parsing is unlikely. Therefore, a 'Parsing Rule' must be created within the Cortex XSIAM console (associated with the data source) to specifically extract and normalize the relevant fields from the complex JSON structure. Option A is incorrect as XSIAM doesn't automatically parse arbitrary JSON over syslog without specific parsing rules. Option B is incorrect; the XDR Agent port is for agent communication, not arbitrary JSON ingestion. Option D is a valid workaround but adds complexity on the application side, whereas XSIAM and Broker VM can handle the parsing. Option E bypasses the Broker VM, which might be acceptable for some scenarios but doesn't answer how the Broker VM handles it.
NEW QUESTION # 60
A large enterprise uses XSIAM for threat detection. They've detected multiple instances of 'Suspicious API Call' alerts originating from a specific internal application. These alerts are high volume but often represent legitimate (though unusual) behavior. The SOC wants to reduce the criticality of these specific alerts while maintaining the detection logic for other applications. Which set of XSIAM content optimization actions are most appropriate to achieve this goal? (Select all that apply)
Answer: B,C
Explanation:
Options B and C are the most appropriate content optimization actions. Option B (Negative Additive Score Change): This directly reduces the score of specific alerts, lowering their criticality and helping to de-prioritize them in the SOC queue without losing the detection. Using a high 'Order' ensures it's applied after initial scoring. Option C (Multiplicative Score Change with Reputation List): This is a scalable and best- practice approach. By defining the legitimate application's entities in a reputation list and applying a multiplicative factor less than 1.0, you proportionally reduce the score for all related alerts. This is dynamic and can be reused. Option A (Modify Detection Rule): While it would stop the alerts, it's generally not recommended for 'legitimate but unusual' behavior. It creates a blind spot. If the behavior changes to truly malicious, the detection would be missed. Content optimization often aims to reduce noise, not eliminate detection. Option D (Automation Playbook): This addresses alert handling after scoring and triage. It doesn't reduce the initial criticality or visibility in the queue; it just automates closure, which might still mean analysts see them initially. Option E (Alert Grouping): While useful for managing alert volume and reducing fatigue, it doesn't directly reduce the criticality score of the individual alerts. It helps in incident management but isn't a direct scoring optimization.
NEW QUESTION # 61
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