XSIAM-Engineer Exam PDF with Questions and Answers – [P2PExam]

1. Paloalto Networks XSIAM-Engineer Palo Alto Networks XSIAM Engineer Questions & Answers PDF (Demo Version – Limited Content) For More Information – Visit link below: https://p2pexam.com/ Visit us at: https://p2pexam.com/xsiam-engineer

2. Latest Version: 6.0 Question: 1 A security analyst is investigating a suspected lateral movement event within a corporate network. XSIAM has generated a high-fidelity alert based on a behavioral indicator of compromise (BIOC) rule. The alert details indicate an unusual process spawning activity followed by a successful SMB connection to a domain controller from a non-privileged workstation. The current BIOC rule for 'Lateral Movement via SMB' triggers on 'Process.CommandLine contains 'net use' AND Network.Protocol == 'SMB' AND Network.DestinationAddress in 'DomainControllersGroup". This rule has a high false positive rate due to legitimate administrative activities. Which of the following modifications to the BIOC rule would most effectively reduce false positives while maintaining detection efficacy for malicious lateral movement attempts, considering the XSIAM context? A. Add an exclusion for ‘User.IsInGroip(‘IT_Admins’)’ to the existing rule. B. Modify the rule to 'Process.CommandLine contains 'net use' AND Network.Protocol == 'SMB' AND Network.DestinationAddress in 'DomainControllersGroup' AND Process.ParentProcess.Name != 'explorer.exe". C. Implement a new BIOC rule that correlates 'Process.Name == 'cmd.exe' OR Process.Name 'powershell.exe" with 'Network.Protocol 'SMB' AND Network.DestinationAddress in 'DomainControllersGroup" and a low-reputation 'Process.ParentProcess.lmageName'. D. Increase the severity of the existing rule and add a playbook action to automatically block the source IP address. E. Remove the 'Network.DestinationAddress in 'DomainControllersGroup" condition to make the rule more general. Answer: C Explanation: Option C offers the most effective approach. Simply excluding IT admins (A) might miss compromised admin accounts. Modifying parent process (B) is too restrictive and might still generate FPs. Increasing severity (D) doesn't address FPs. Removing the destination address condition (E) would drastically increase FPs. Option C leverages behavioral correlation, looking for suspicious command execution (cmd.exe/powershell.exe) leading to SMB connections to sensitive assets, especially when initiated by a low-reputation parent process, which is a common pattern for lateral movement by attackers. This leverages XSIAM's ability to correlate diverse data sources for more accurate detection. Question: 2 An organization is deploying a new web application and wants to ensure robust detection of common web-based attacks using XSIAM. They have observed several attempts of SQL Injection and Cross-Site Scripting (XSS) during pre- production testing. To optimize their detection content, which of the following XSIAM content Visit us at: https://p2pexam.com/xsiam-engineer

3. management strategies would be most effective for creating high-fidelity detection rules for these attack types, leveraging both IOCs and BIOCs? A. Rely solely on out-of-the-box XSIAM rules for web attacks, as they are generally comprehensive. B. Create custom IOC rules based on known malicious IP addresses and URLs found in threat intelligence feeds related to web attacks. C. Develop BIOC rules that analyze web server logs for unusual HTTP request parameters, abnormal response codes, and sequences of requests indicative of SQLi or XSS payloads, while also incorporating IOCs for known attack patterns. D. Implement a simple keyword-based search in XSIAM for common SQLi keywords like 'SELECT FROM' and XSS keywords like '<script>'. E. Configure network-based firewalls to block all traffic containing 'SQL' or 'XSS' in the payload. Answer: C Explanation: Option C is the most effective. While out-of-the-box rules (A) are a good starting point, custom rules are often needed for specific applications. IOCs (B) are good for known threats but won't catch novel or polymorphic attacks. Simple keyword searches (D) are prone to high false positives and evasion. Blocking all 'SQL' or 'XSS' (E) will undoubtedly break legitimate application functionality. Option C combines the strength of behavioral analysis (BIOCs) by looking at patterns and sequences that indicate an attack, which is crucial for SQLi and XSS, with the precision of IOCs for known attack signatures. This hybrid approach provides robust and adaptable detection. Question: 3 A threat actor has gained initial access to an endpoint via a phishing email and is attempting to establish persistence. The XSIAM agent on the endpoint observes the following sequence of events: Which of the following XSIAM BIOC rules would be most effective in detecting this specific persistence mechanism, prior to the 'Registry.Key' modification being observed, assuming the goal is to catch the initial malicious execution chain? A. B. C. D. E. Visit us at: https://p2pexam.com/xsiam-engineer

4. Answer: D Explanation: Option D is the most effective for detecting the malicious execution chain leading to persistence. Option A is too broad and could lead to false positives (e.g., legitimate PowerShell scripts launched by Word). Option B is too early in the kill chain and only indicates opening a document. Option C detects the persistence after it's established, which is less ideal for preventing it. Option E only detects the initial opening, not the malicious execution. Option D specifically targets the suspicious activity of PowerShell being spawned by Word with an encoded command, a common technique for malicious document macros to execute payloads. This BIOC focuses on a high-fidelity indicator of malicious activity rather than just the initial access or the final persistence artifact. Question: 4 A critical server application occasionally executes system-level commands for legitimate maintenance tasks, which sometimes resemble malicious activity. An existing XSIAM BIOC rule flags any 'Process.CommandLine contains 'whoami' OR Process.CommandLine contains 'net user'‘ on critical servers. This rule is generating too many false positives. To reduce these false positives without missing actual attacks, how should the XSIAM engineer optimize this rule using context from the XDR dataset? A. Disable the rule entirely on critical servers. B. Modify the rule to ‘Process.CommandLine contains 'whoami' AND NOT Process.ParentProcess.Name 'SystemUpdateService.exe'‘. C. Add a global exception for the critical server IP addresses. D. Adjust the rule to correlate 'Process.CommandLine contains 'whoami' OR Process.CommandLine contains 'net user" with a 'Process.lmageName' that is not on a trusted application whitelist, and potentially with an unusual 'User.AccountName'. E. Change the rule's severity to 'Low' so it generates fewer high-priority alerts. Answer: D Explanation: Option D is the most robust and effective solution. Disabling the rule (A) or adding a global exception (C) would create a blind spot. Option B is better but might still miss other legitimate processes or be circumvented by attackers. Changing severity (E) doesn't solve the false positive issue, only prioritizes them differently. Option D leverages contextual information from XDR by looking for command execution from untrusted binaries or by unusual user accounts. This allows for more precise detection by identifying suspicious deviations from normal behavior rather than just the presence of certain commands, significantly reducing false positives while maintaining detection capability. Question: 5 An organization is struggling with alert fatigue from a poorly tuned XSIAM detection rule for suspicious network connections. The current rule triggers on 'Network.Protocol == 'TCP' AND Network.DestinationPort == '4444" for all endpoints. This port is legitimately used by a legacy application for internal communication, but it's also a common C2 port. The security team wants to Visit us at: https://p2pexam.com/xsiam-engineer

5. optimize this rule to be more precise. Which of the following XSIAM content optimization strategies would best address this scenario? A. Create an allow-list for specific source IP addresses that legitimately use port 4444. B. Modify the existing rule to include 'AND NOT Network.DestinationAddress in 'LegacyAppServersGroup". C. Create two separate rules: one for the legacy application allowing port 4444, and a higher-severity rule for 'Network.Protocol 'TCP' AND Network.DestinationPort '4444" that also correlates with 'Process.Reputation 'unknown' OR Process.Reputation 'malicious". D. Change the rule to only trigger during non-business hours. E. Remove the rule as port 4444 is too ambiguous to detect C2. Answer: C Explanation: Option C is the most effective content optimization strategy. Option A and B are forms of allow-listing, which can work, but Option C provides a more robust and granular approach. Option C allows for the legitimate traffic to be ignored while specifically targeting the suspicious activity by correlating the port usage with the reputation of the process initiating the connection. This leverages XSIAM's rich process metadata and reputation services to significantly reduce false positives from the legacy application while effectively detecting actual C2 activity. Option D is not effective for C2, and Option E would create a significant blind spot. Question: 6 A sophisticated APT group is known to use custom exfiltration techniques involving DNS tunneling. They typically encode data within legitimate-looking DNS queries to external command and control (C2) domains that are rarely queried by legitimate enterprise applications. To detect this in XSIAM, a security engineer needs to craft a BIOC rule. The rule should focus on high-volume, repetitive DNS queries to unknown or suspicious domains, especially when originating from non-DNS server assets. Which combination of XSIAM XDR fields and query logic would be most effective for this BIOC, minimizing false positives? A. B. C. D. E. Visit us at: https://p2pexam.com/xsiam-engineer

6. Answer: C Explanation: Option C is the most effective and sophisticated BIOC for detecting DNS tunneling. Option A relies on known malicious domains, which might change. Option B specifically looks for TXT records and high volume, which is better but doesn't account for legitimate TXT use or source of queries. Option D is too simplistic. Option E focuses on response codes and process reputation, which is useful but might miss successful exfiltration or legitimate unknowns. Option C combines multiple strong indicators: outbound DNS, queries not seen from legitimate DNS servers, queries not in known good domains (leveraging XSIAM's external reputation), unusually long query names (indicative of encoded data), queries not from the legitimate DNS service itself, and a high volume from a single host within a short time window. This multi-faceted approach significantly reduces false positives while effectively targeting the described exfiltration technique. Question: 7 An organization is migrating legacy detection logic from a SIEM to XSIAM. One critical rule identifies a specific sequence of system calls indicative of kernel-level rootkit activity: 'Process_Creation -> File_Write_to_System32 -> Driver_Load'. In XSIAM, how can this multi- stage behavioral indicator be most effectively implemented as a BIOC rule to ensure high fidelity and minimal false positives, considering the distributed nature of XDR data? A. Create three separate rules, one for each event type, and manually correlate the alerts in the XSIAM console. B. Develop a single BIOC rule using XQL's 'pattern' command to specify the ordered sequence of events, ensuring specific attributes like ‘Process.PlD or Host.lD match across stages, and apply filtering for legitimate activity. C. Use an IOC rule to detect the presence of known rootkit file hashes in System32. D. Focus only on detecting 'Driver_Load' events, as this is the final stage of rootkit installation. E. Write a Python script that pulls all Process, File, and Driver events from XSIAM's API and performs correlation outside the platform. Answer: B Explanation: Option B is the most effective and native XSIAM approach. Option A would lead to significant manual effort and delayed detection. Option C is an IOC approach, which is reactive and won't catch unknown rootkits. Option D misses crucial preceding stages. Option E bypasses XSIAM's powerful correlation capabilities and adds unnecessary complexity. XSIAM's XQL (Cortex Query Language) with the ‘pattern’ command is specifically designed for multi-stage threat detection. It allows defining a sequence of events, linking them by common identifiers (like PID, Host ID, User ID), and applying detailed filters to exclude benign activities, resulting in high-fidelity BIOCs for complex attack patterns like rootkit installation. Question: 8 Visit us at: https://p2pexam.com/xsiam-engineer

7. An organization relies heavily on cloud infrastructure, and a new XSIAM deployment is underway to monitor AWS accounts. A key requirement is to detect 'data exfiltration via S3 bucket public exposure'. This involves correlating an 'AWS.CloudTrail.EventName' indicating a change in S3 bucket policy to public, with subsequent high-volume ‘AWS.S3.BytesTransferred' events and Network.Protocol == 'HTTPS" outbound connections from compromised instances. Which XSIAM content optimization approach effectively addresses this multi-cloud, multi-event type detection scenario? A. Create a simple IOC rule to alert on any 'AWS.S3.BucketPolicy' change event. B. Implement separate BIOC rules for each event type (S3 policy change, high S3 transfer, outbound HTTPS) and manually review each alert. C. Utilize XSIAM's cross-domain correlation capabilities by crafting a BIOC rule that leverages XQL ‘join' or ‘pattern' operations across , , and 'network_connections’ datasets, filtering for 'PublicRead' or 'PublicWrite' ACLs on S3 and significant 'bytes_transferred' from non- authorized IPs. D. Configure AWS CloudWatch alarms directly for S3 bucket policy changes and rely on those for detection. E. Only monitor 'AWS.S3.BytesTransferred’ from EC2 instances, ignoring S3 bucket policy changes. Answer: C Explanation: Option C is the most comprehensive and effective approach. Option A is too broad and generates false positives without context. Option B leads to alert fatigue and misses the crucial correlation. Option D provides alerts but lacks the rich context and automation of XSIAM's XDR. Option E ignores a critical precursor. XSIAM excels at cross-domain correlation, allowing engineers to write sophisticated XQL queries that join or pattern-match events from various sources (Cloud Trail, S3 data, network events) and different security domains (cloud, network, endpoint). This enables precise detection of complex attacks like data exfiltration that span multiple layers of an organization's infrastructure. Question: 9 A red team exercise revealed that traditional IOCs (e.g., hash, IP, domain) for a known malware family were easily bypassed by polymorphic variants. The malware, however, consistently performs a unique sequence of API calls to inject code into legitimate processes: 'NtOpenProcess' -> 'NtAllocateVirtualMemory' -> 'NtWriteVirtualMemory' -> 'NtCreateRemoteThread'. To counter this, an XSIAM engineer needs to create a high-fidelity BIOC. Which of the following XQL queries best represents this behavioral pattern while minimizing false positives from legitimate applications performing similar operations? A. B. C. D. Visit us at: https://p2pexam.com/xsiam-engineer

8. E. Answer: E Explanation: Option E is the most comprehensive and effective XQL query for this complex BIOC. Option A is too generic and will generate many false positives. Option B is closer but lacks crucial filters for common legitimate processes that might perform similar actions (e.g., debuggers, security tools) and doesn't specify a time window, which is critical for behavioral sequences. Option C is too specific to only the last step and might miss the full chain. Option D is too broad and only relies on reputation. Option E correctly uses the ‘pattern' command to define the exact sequence of API calls, ensuring they occur within a specific ‘time_window’ and 'by' the same 'host_id' and ‘process.pid'. Critically, it includes exclusions for ‘target_process.name’ (common legitimate injection targets like csrss.exe, winlogon.exe, explorer.exe, dwm.exe) and filters for ‘stage_l .process.reputation != 'trusted" to reduce false positives while accurately targeting malicious injection attempts. Question: 10 A financial institution uses XSIAM for endpoint and network security. They recently experienced a sophisticated supply chain attack where a digitally signed, but malicious, update utility was distributed. Traditional file hash IOCs failed due to unique compilation per target. The attacker then used this utility to install a persistent backdoor. To detect such future attacks, which combination of XSIAM content optimization strategies would be most effective? A. Focus solely on network-based IOCs (C2 IPs, domains) as they are less prone to polymorphism. B. Implement BIOC rules for 'Parent-Child Process Anomalies' (e.g., legitimate signed utility spawning cmd.exe, PowerShell, or unusual network connections), 'Persistence Mechanism Detection' (e.g., new registry Run keys from unsigned binaries), and leverage XSIAM's 'Trusted Signer' whitelisting with 'Signature Verification Failure' detection for any unsigned modules loaded by signed applications. C. Increase the frequency of endpoint scans for known malware signatures. D. Create a comprehensive list of all legitimate software hashes and alert on any executable not on the list. E. Disable all behavioral rules to reduce alert fatigue and rely only on network perimeter defenses. Answer: B Explanation: Option B provides the most robust and multi-layered defense against such sophisticated attacks. Option A is insufficient as network IOCs can also change. Option C is reactive and easily bypassed by polymorphic malware. Option D is impractical due to the constantly changing software landscape and high false positives. Option E creates massive blind spots. Option B combines several critical BIOCs: detecting unusual child processes from seemingly legitimate parents, identifying common persistence Visit us at: https://p2pexam.com/xsiam-engineer

9. mechanisms when initiated by suspicious processes, and crucially, leveraging XSIAM's ability to monitor digital signatures. Detecting 'Signature Verification Failure' or 'Unsigned Module Loaded by Signed Process' is a powerful BIOC for supply chain attacks where a signed legitimate application might load or execute malicious unsigned components, which is difficult to bypass. Visit us at: https://p2pexam.com/xsiam-engineer

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