Free Palo Alto Networks XSIAM-Engineer Exam Dumps Questions & Answers

An XSIAM engineer needs to implement a scoring rule that dynamically adjusts alert severity based on the 'asset_criticality' field, which is populated via an external CMDB integration. Alerts associated with assets marked 'High' criticality should receive a significant score boost, while 'Low' criticality assets should see a reduction. Which of the following XQL-like logic within a scoring rule's condition and action configuration best supports this scenario, assuming 'alert.asset_criticality' is a field that holds 'High', 'Medium', or 'Low'?

• A.  Condition: 'alert.asset_criticality = 'High'' Action: Multiplicative x2.0; Condition: 'alert.asset_criticality = 'Low" Action: Multiplicative x0.5. Configure as two separate scoring rules.
• B.  Condition: 'alert.asset_criticality in ('High', 'Low') Action: (alert.asset_criticality = 'High') then SetTotalScore(90) else SetTotalScore(30)'.
• C.  Condition: 'alert.asset_criticality = 'High" Action: Additive +30; Condition: 'alert.asset_criticality = 'Low" Action: Additive -15. Configure as two separate scoring rules with distinct orders.
• D.  Condition: 'alert.asset_criticality = 'High'' Action: Additive +'alert.base_score 0.5; Condition: 'alert.asset_criticality = 'Low" Action: Additive '-alert.base_score 0.2.
• E.  Use a single scoring rule with a complex XQL case statement:
  

A critical national infrastructure (CNI) provider is deploying Palo Alto Networks XSIAM within a highly regulated environment. This environment demands extreme resilience, fault tolerance, and a zero-downtime objective, even during major hardware failures or planned maintenance. From a hardware planning perspective, what specific design principles must be rigorously adhered to, beyond typical redundancy?

• A.  Integrating with an uninterruptible power supply (UPS) and generator backup system that can sustain the entire XSIAM infrastructure for a minimum of 72 hours without external power.
• B.  Implementing a 'N+2' redundancy model for all XSIAM cluster nodes, storage arrays, and network devices, far exceeding standard 'N+l' recommendations.
• C.  Deploying the XSIAM cluster across multiple distinct, geographically separated data centers (active-active configuration) with independent power, cooling, and network infrastructure, and a robust data replication mechanism.
• D.  Establishing a fully independent, identical 'cold standby' XSIAM cluster in a separate physical location, requiring manual failover in case of a catastrophic event.
• E.  Utilizing only 'hardened' or 'military-grade' server hardware certified to withstand extreme environmental conditions and electromagnetic interference.

During the planning phase for a new XSIAM deployment, an organization identifies that a critical internal application generates highly sensitive proprietary logs in a custom JSON format, which frequently changes due to agile development cycles. XSIAM's standard data connectors do not fully support this dynamic format out-of-the-box. What is the most robust approach to ensure reliable and scalable ingestion of these logs into XSIAM?

• A.  Developing a custom log forwarder using a scripting language (e.g., Python) that transforms the JSON into a XSlAM-compatible CEF or LEEF format before sending it to the XSIAM broker.
• B.  Requesting Palo Alto Networks Professional Services to develop a bespoke data connector for this specific application, regardless of cost implications.
• C.  Manual parsing of logs within XSIAM's AQL queries for each incident, relying on regular expression matching.
• D.  Utilizing a generic syslog forwarder and hoping XSIAM's machine learning capabilities can automatically parse the custom JSON.
• E.  Modifying the internal application to output logs in a standard format like Syslog RFC 5424, even if it requires significant development effort.

A large enterprise uses XSIAM for comprehensive security. They have a strict policy against the use of insecure authentication protocols like NTLMv1 , even for internal services. They want to create an ASM rule to detect any internal server or application attempting to authenticate using NTLMv1. Given that XSIAM collects authentication logs from various sources (Active Directory, Linux authentication, network authentications), which of the following XQL approaches would be most effective for detecting NTLMv1 usage across their distributed environment?

• A. 
• B. 
• C.  Combine insights from 'xdr_authentication_logs' (for protocol details) and 'xdr_network_sessions' (for application protocol and potential deep packet inspection insights if available) to precisely identify NTLMv1. An example would be:
  
• D. 
• E. 

A cybersecurity firm specializing in managed security services (MSSP) plans to offer XSIAM as a service to its diverse clientele. This requires a multi-tenant XSIAM deployment. The MSSP needs to ensure strict data segregation, performance isolation for each tenant, and efficient resource utilization across tenants. From a hardware perspective, what are the primary considerations to achieve these objectives, and what is a potential pitfall?

• A.  Utilizing a hyperconverged infrastructure (HCI) solution with robust virtualization capabilities and resource governance features to logically isolate tenants, with a pitfall of potential 'noisy neighbor' issues if not properly configured.
• B.  Deploying dedicated physical server hardware for each major tenant to ensure strict performance isolation, with a pitfall of high capital expenditure and underutilization of resources.
• C.  Relying solely on XSIAM's built-in multi-tenancy features without additional hardware-level isolation, with a pitfall of insufficient performance guarantees and potential resource contention between tenants.
• D.  Procuring high-end GPU servers to accelerate tenant-specific machine learning models, with a pitfall of high power consumption and limited applicability to all XSIAM workloads.
• E.  Implementing a container orchestration platform like Kubernetes on bare-metal servers to provide granular resource limits for each tenant, with a pitfall of increased operational complexity and learning curve.