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Palo Alto Networks XSIAM Engineer Sample Questions (Q421-Q426):
NEW QUESTION # 421
An organization is considering a hybrid XSIAM deployment, where ingestion and initial processing occur on-premises, but long-term data retention and advanced analytics (e.g., complex ML models requiring significant compute) are offloaded to a public cloud provider. What are the key hardware planning considerations on the on-premises side to facilitate this hybrid model effectively?
Answer: A,D,E
Explanation:
For an effective hybrid XSIAM deployment with on-premises ingestion and cloud analytics/retention, several hardware considerations on-premises are crucial. Sizing on-premises hardware for peak ingestion and providing buffer storage (A) is vital to prevent data loss or backpressure. A dedicated, high-bandwidth, low-latency network connection (B) is absolutely critical for efficient and timely data transfer to the cloud. Powerful CPUs and ample RAM on-premises (C) are necessary to perform initial data processing (parsing, normalization, basic indexing) before sending data to the cloud, offloading compute from the cloud and ensuring data is in a usable format upon arrival. While compression appliances (D) can help with costs, they are secondary to the fundamental infrastructure requirements. GPU passthrough (E) is relevant for ML but contradicts the premise of offloading advanced analytics to the cloud, making it less of a primary on-premises hardware concern for this specific hybrid model.
NEW QUESTION # 422
An XSIAM administrator is reviewing the audit logs for user activity and notices suspicious API calls originating from a compromised service account. The API key associated with this service account has 'Security Operations Center - Admin' permissions. The immediate action is to revoke the compromised API key. Which of the following XSIAM commands or API operations would be used to revoke a specific API key, assuming you have the necessary administrative privileges?
Answer: B,D
Explanation:
Both the XSIAM UI and the XSIAM API provide mechanisms to revoke API keys. Option B describes the direct IJI approach, which is straightforward for administrators. Option C describes the typical REST API approach for deleting a resource, where DELETE requests are used to revoke or remove API keys. Option A is a pseudocode function call that might be part of an SDK, but not a direct API endpoint. Option D is an extreme measure that would disrupt all API integrations and is not the targeted way to revoke a single key. Option E is an unsupported and dangerous method of configuration management.
NEW QUESTION # 423
A critical XSIAM indicator rule detects 'Excessive Failed Login Attempts' on sensitive servers. The rule aggregates events and triggers if a user has more than 10 failed attempts within 5 minutes on a specific Currently, the rule frequently triggers for service accounts due to misconfigurations or temporary network issues, leading to alert fatigue. How can this rule be optimized using XSIAM's capabilities to reduce false positives for service accounts while maintaining efficacy for user accounts?
Answer: B,C
Explanation:
Both C and D are strong, effective methods for addressing this complex scenario. C: Create Separate Rules: This is a straightforward and effective way to apply different logic based on account type. You create one rule for standard user accounts (with the lower threshold) and another, identical rule but with a higher threshold, specifically targeting identified service accounts. This clearly separates the monitoring logic. D: Leverage Context Tables/Lookup Lists: This is a more elegant and scalable solution, especially if you have many service accounts or different thresholds for various types of service accounts. You maintain a 'Context Table' (also known as a 'Lookup List') in XSIAM that maps service account names to their desired failed login thresholds. The indicator rule's XQL query can then 'join' or "lookup' this table to dynamically apply the correct threshold based on the 'user_name' in the event. This centralizes threshold management and reduces the need for multiple static rules. Option A reduces sensitivity for all accounts, potentially missing user-based brute-force. Option B completely ignores service account issues, which can still be indicators of compromise. Option E is a post-detection automation, not a rule optimization; it still generates the false positive and consumes alert triage time.
NEW QUESTION # 424
Cortex XSIAM has not received any logs for 30 minutes from a Palo Alto Networks NGFW named
"MainFW." An engineer wants to create an alert for this scenario.
Correlation rule settings include:
Time Schedule: Every 30 minutes
Query Timeframe: 30 minutes
Action: Generate alert
Alert Name: No logs received from MainFW in the past 30 minutes
Which query should be used in the correlation rule?
Answer: A
Explanation:
The correct query is the one using preset = metrics_view with
comp sum(total_event_count) as total_events by _reporting_device_name and filtering total_events = 0.
This query directly checks event counts reported by the NGFW ("MainFW"). If no logs are received in the last 30 minutes, the total event count will be 0, which triggers the correlation rule alert.
NEW QUESTION # 425
A critical objective for a new XSIAM deployment is to enable real-time detection of insider threats, specifically focusing on data exfiltration attempts. This requires monitoring sensitive file access on endpoints, cloud storage interactions (e.g., OneDrive, Google Drive), and email activity (Microsoft 365 Exchange Online). Which data sources, in order of criticality for this objective, should be prioritized for integration into XSIAM, and what specific data points are most crucial?
Answer: D
Explanation:
For insider threat detection related to data exfiltration, the most critical data sources are those directly monitoring access to and movement of sensitive data. Endpoint logs (file access, process activity) are paramount for detecting local exfiltration attempts. CASB logs provide visibility into cloud storage activities, which are common exfiltration vectors. Email logs (M365 Audit) are crucial for detecting data sent via email. The specified data points (username, file path, cloud app, email recipient, attachment hash) are essential for building effective detection rules and forensic analysis.
NEW QUESTION # 426
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