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[Hardware] New XSIAM-Engineer Test Sample, New XSIAM-Engineer Test Prep

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【Hardware】 New XSIAM-Engineer Test Sample, New XSIAM-Engineer Test Prep

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Palo Alto Networks XSIAM-Engineer Exam Syllabus Topics:
TopicDetails
Topic 1
  • Integration and Automation: This section of the exam measures skills of SIEM Engineers and focuses on data onboarding and automation setup in XSIAM. It covers integrating diverse data sources such as endpoint, network, cloud, and identity, configuring automation feeds like messaging, authentication, and threat intelligence, and implementing Marketplace content packs. It also evaluates the ability to plan, create, customize, and debug playbooks for efficient workflow automation.
Topic 2
  • Content Optimization: This section of the exam measures skills of Detection Engineers and focuses on refining XSIAM content and detection logic. It includes deploying parsing and data modeling rules for normalization, managing detection rules based on correlation, IOCs, BIOCs, and attack surface management, and optimizing incident and alert layouts. Candidates must also demonstrate proficiency in creating custom dashboards and reporting templates to support operational visibility.
Topic 3
  • Maintenance and Troubleshooting: This section of the exam measures skills of Security Operations Engineers and covers post-deployment maintenance and troubleshooting of XSIAM components. It includes managing exception configurations, updating software components such as XDR agents and Broker VMs, and diagnosing data ingestion, normalization, and parsing issues. Candidates must also troubleshoot integrations, automation playbooks, and system performance to ensure operational reliability.
Topic 4
  • Planning and Installation: This section of the exam measures skills of XSIAM Engineers and covers the planning, evaluation, and installation of Palo Alto Networks Cortex XSIAM components. It focuses on assessing existing IT infrastructure, defining deployment requirements for hardware, software, and integrations, and establishing communication needs for XSIAM architecture. Candidates must also configure agents, Broker VMs, and engines, along with managing user roles, permissions, and access controls.

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Palo Alto Networks XSIAM Engineer Sample Questions (Q93-Q98):NEW QUESTION # 93
An organization is performing a hardware sizing exercise for a Palo Alto Networks XSIAM deployment, anticipating 250,000 security events per second (EPS) on average, with potential spikes to 500,000 EPS during security incidents. The security team also expects to run complex analytical queries that involve joining data from multiple sources over a 3-month period, often requiring custom aggregations. Which of the following hardware characteristics would be the most critical to prioritize for the XSIAM cluster nodes to handle this workload effectively?
  • A. NVMe SSDs with the highest possible IOPS and lowest latency, even if it means sacrificing some CPU and RAM capacity.
  • B. High clock speed CPUs (e.g., 3.0+ GHz) with a moderate number of cores (e.g., 16-24) to optimize single-thread performance for parsing and normalization.
  • C. Extremely fast network interfaces (e.g., 200 GbE) to handle the massive ingress rate, even if CPU and RAM specifications are slightly lower.
  • D. Maximum possible RAM capacity per node (e.g., 768 GB - 1 TB+) to keep larger datasets in memory for faster query execution.
  • E. A balance of high core count CPUs (e.g., 32-64 cores) and large amounts of high-speed RAM (e.g., 512 GB+) to facilitate parallel processing for both ingestion and complex analytical queries.
Answer: E
Explanation:
This scenario describes both high ingestion rates (requiring processing power) and complex analytical queries (requiring significant computational resources and memory). XSIAM leverages distributed computing for these tasks. Therefore, a balance of high core count CPUs (for parallel processing of ingestion and queries) and large amounts of high-speed RAM (to hold working sets for complex aggregations and joins) is paramount (C). While high clock speed CPUs (A) are good for some tasks, the sheer volume and complexity necessitate parallelization provided by more cores. Maximum RAM (B) is beneficial but insufficient without adequate CPU. Extremely fast network interfaces (D) are important for ingress but useless if the cluster can't process the data. NVMe SSDs (E) are crucial for I/O but don't address the computational and memory demands of complex analytics.

NEW QUESTION # 94
A new regulatory requirement mandates the obfuscation of specific Personally Identifiable Information (PII) fields (e.g., 'customer_ssn', 'patient_id') from logs originating from an application before they are stored in the XSIAM Data Lake. The raw logs are in a custom XML format. Which XSIAM Data Flow operation(s) would be most suitable to extract these fields, apply obfuscation, and ensure the obfuscated data is correctly indexed?

  • A. Option D
  • B. Option E
  • C. Option A
  • D. Option B
  • E. Option C
Answer: C
Explanation:


NEW QUESTION # 95
A critical XSIAM automation rule is designed to automatically enrich incidents with threat intelligence based on observed IP addresses. The rule triggers a playbook that makes multiple external API calls to different Tl sources. Lately, some incidents are not being enriched, and the XSIAM automation logs show 'Timeout errors for the associated playbook runs. You suspect a bottleneck in sequential API calls and potentially network latency to certain Tl providers. How would you debug and optimize this for efficiency and resilience?
  • A. Implement asynchronous API calls within the XSOAR playbook using Python's *asyncio' or by leveraging 'demisto.executeCommand' with the 'async=trues argument for independent commands, followed by 'demisto.results' to collect outputs.
  • B. Utilize XSOAR's built-in 'Troubleshooting' and 'Metrics' dashboards to monitor the average execution time of the playbook and identify which API calls are contributing most to the timeouts.
  • C. Distribute the threat intelligence lookup across multiple XSOAR engines, assigning specific Tl sources to different engines via engine groups.
  • D. Prioritize the most critical Tl sources and only call those in the initial enrichment phase, deferring less critical lookups to a secondary, lower-priority automation.
  • E. Increase the timeout settings for each external API call within the playbook's integration configurations or script logic.
Answer: A,B
Explanation:
Timeout errors suggest that the playbook is taking too long to execute, especially with multiple sequential API calls. Implementing asynchronous API calls (A) allows multiple lookups to happen concurrently, significantly reducing overall execution time and improving resilience to latency in individual calls. This is a core optimization for MO-bound operations. Additionally, using XSOAR's monitoring dashboards (E) is crucial for debugging: it provides direct insights into which specific tasks or API calls are causing the delays, guiding targeted optimization efforts. While B might temporarily mitigate some timeouts, it doesn't solve the underlying efficiency problem. C is for horizontal scaling of engines, not internal playbook parallelism. D is a workflow optimization but doesn't directly address the performance bottleneck.

NEW QUESTION # 96
A security architect is designing the integration of XSIAM with an on-premises vulnerability management solution that provides vulnerability scan results in an XML format. The XSIAM team wants to ingest these results, parse them, and use the 'CVSS score' and 'affected asset IP' fields to enrich alerts related to those assets. Which XSIAM integration component and subsequent processing step are crucial for this scenario?
  • A. Upload the XML files directly to XSIAM as a threat intelligence feed.
  • B. Use the XSIAM Data Collector to ingest the XML files as raw data, then apply a XSIAM parser with an XSLT transformation to extract relevant fields.
  • C. Manually review the XML files and create XSIAM lookup tables for CVSS scores.
  • D. Configure a syslog server to receive the XML data from the vulnerability scanner and forward it to XSIAM.
  • E. Develop a Python script to convert the XML to JSON, then push the JSON data to XSIAM via the HTTP Event Collector.
Answer: B
Explanation:
Option A is the most accurate and efficient approach. XSIAM Data Collectors can ingest various file formats, including XML. The key is applying a custom parser (potentially using XSLT for XML transformation) within XSIAM to extract the structured data (CVSS score, affected IP) from the XML. This allows XSIAM to properly index and use these fields for enrichment. Option B is unlikely to handle XML parsing effectively via syslog. Option C is a workaround but less native than XSIAM's parsing capabilities. Option D is incorrect for structured vulnerability data. Option E is manual and not scalable.

NEW QUESTION # 97
A highly critical zero-day exploit has been identified, and your XSIAM tenant has just received a new detection rule update for it. However, during initial testing in a controlled environment, you observe that this new rule is generating false positives when specific legitimate internal diagnostic tools are run, triggering an alert with 'Alert Name: Critical_Exploit_Attempt_CVE-2023-XYZ'. You need to immediately prevent these specific false positives from escalating within XSIAM's alert lifecycle while ensuring the rule remains active for actual malicious activities. What is the most effective and recommended XSIAM configuration to achieve this, considering the high criticality of the actual exploit?
  • A. Temporarily disable the 'Critical_Exploit_Attempt_CVE-2023-XYZ' detection rule until a more refined version is released by Palo Alto Networks.
  • B. Create an 'Exclusion' within the relevant 'Detection Rule' settings, specifying conditions unique to the legitimate diagnostic tools (e.g., 'process_name = 'diag_tool.exe" AND 'user_name = 'admin_user") for the 'Critical_Exploit_Attempt_CVE-2023-XYZ' rule.
  • C. Implement an XSOAR playbook that automatically closes any incident with 'Critical_Exploit_Attempt_CVE-2023-XYZ' if the associated host belongs to a specific 'diagnostic_servers' asset group.
  • D. Develop a new 'Suppression Rule' in 'Alert Management' that matches 'alert_name = AND 'destination_port= '8080" (where the diagnostic tool communicates) and set its action to 'Drop Alert'.
  • E. Lower the severity of the 'Critical_ExpIoit_Attempt_CVE-2023-XYZ' alert to 'Informational' globally until the false positive issue is resolved.
Answer: B
Explanation:
Option B is the most effective. 'Exclusions' directly within the 'Detection Rule' configuration allow you to define conditions under which the rule should NOT generate an alert. This is precisely designed for false positive suppression. By specifying conditions unique to the legitimate activity (like process name and user), you prevent specific false positives while allowing the rule to detect actual threats. Option A lowers severity globally, which is dangerous for a critical exploit. Option C (Suppression Rule) acts on alerts after they are generated, whereas Exclusion prevents them from being generated in the first place, which is more efficient for known false positives. Option D creates a major security gap. Option E (XSOAR playbook) can be used for post-alert automation, but an Exclusion is more direct and efficient for preventing the alert generation itself.

NEW QUESTION # 98
......
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