Palo Alto Networks Security Operations Professional 온라인 연습
최종 업데이트 시간: 2025년12월09일
당신은 온라인 연습 문제를 통해 Paloalto Networks SecOps-Pro 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 SecOps-Pro 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 313개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
All listed options are crucial for comprehensive recovery and future prevention after a major incident like a phishing campaign leading to data exfiltration.
A (MFA): Directly addresses account compromise, a primary vector in phishing.
B (Cortex XDR Threat Hunting): Ensures no lingering threats and helps understand the full scope of compromise, aiding eradication and future defense.
C (NGFW Policy Updates): Enhances network-level prevention and control based on lessons learned from the attack's lateral movement and data exfiltration methods.
D (Security Awareness Training): Addresses the human element, which is critical in preventing phishing successes.
E (Patch Management): While not directly related to phishing (unless the phishing delivered an exploit), strong patch management is fundamental to overall security posture and preventing future exploitation of vulnerabilities discovered during the incident.
정답:
Explanation:
The core issue is a known malicious payload from a supply chain attack. Integrating external threat intelligence (B) directly addresses this by allowing the NGFW to dynamically block or alert on known malicious hashes and C2 IPs associated with the attack. While SSL Decryption (A) is good practice, blocking all unknown URLs is overly broad. File blocking (C) is too restrictive and could break legitimate operations. User- IDIApp-ID (D) are valuable for application control but don't directly prevent the download of known malicious files based on their hashes. Increasing WildFire timeout (E) would delay delivery but might not entirely prevent a highly evasive, targeted payload if it bypasses WildFire's initial analysis or is a zero-day.
정답:
Explanation:
Option D is the most suitable and efficient. XSOAR excels at automating tasks across a large number of endpoints. The '!exec- remote-command' (or similar endpoint-management integration command, depending on the specific endpoint integration) allows for remote execution of scripts on designated systems, which is exactly what's needed for eradication.
Option A is for communication.
Option B is for incident creation, not execution.
Option C shows a generic API call, but without a specific integration handling ‘endpoint.execute_script’, it's not as direct as ‘exec-remote-command'.
Option E is highly inefficient and impractical for hundreds of endpoints.

정답:
Explanation:
The encoded PowerShell command and external network connection strongly suggest active compromise and C2 communication. The most immediate and critical step is containment to prevent further damage. Isolating the server (B) using XDR's capabilities directly addresses this by stopping the threat's spread. Decoding the command (A) and collecting forensics (D) are important but come after containment. Vulnerability scanning (C) is a post-incident activity or part of proactive security, not an immediate response to an active compromise. Notifying management (E) is part of communication but not the first technical response.
정답:
Explanation:
While other options have merit in later stages, option B is most effective for initial confirmation and intelligence gathering. Blocking all DNS (A) could disrupt legitimate services. Forensic imaging (C) is crucial but premature for initial confirmation. Quarantining (D) is a containment step, not an initial detection/intelligence gathering one. Waiting for EDR alerts (E) is reactive; proactive configuration (B) on the NGFW, leveraging threat intelligence for DGA, allows for real-time identification and packet capture for immediate analysis and confirmation of C2 communication, which is vital for understanding the threat's nature.
정답:
Explanation:
The 'Post-lncident Activity' phase includes lessons learned and improvements. The scenario specifically points to 'gaps in threat intelligence integration and automated response capabilities' for complex multi-stage attacks.
- A: Implementing Cortex XSOAR playbooks with AutoFocus and WildFire integration directly addresses both gaps. XSOAR automates the enrichment of alerts with context from global threat intelligence (AutoFocus, WildFire) and orchestrates automated responses, significantly enhancing the 'Detection and Analysis' accuracy and the speed/efficiency of 'Preparation' by defining automated actions for future similar incidents. This is precisely about integrating intelligence and automating responses.
- B, C, D, and E are all valid security improvements, but they do not directly address the specific gaps identified (threat intelligence integration and automated response) as effectively as XSOAR and its capabilities. Patching (B), scans (C), and micro-segmentation (D) are about reducing attack surface and improving network controls, while email security (E) focuses on one attack vector. While beneficial, none specifically enhance the integration of threat intelligence for analysis or automate complex, multi-tool responses to APTs like XSOAR does.

정답:
Explanation:
The most significant flaw for reliable and efficient containment, especially with large or rapidly evolving threat intelligence, is option B. Creating individual Address objects and adding them one by one results in a separate API call for each new IP. When dealing with hundreds or thousands of indicators, this generates an excessive number of API calls and significantly prolongs the commit time. Palo Alto Networks firewalls are optimized for bulk operations. For dynamic threat intelligence, it's far more efficient to use a Dynamic Address Group (DAG) or External Dynamic List (EDL) which can consume a text file or URL feed of IPs, minimizing API calls and commit operations, thus ensuring faster and more efficient containment without impacting firewall performance. While other options point to potential issues, none are as critical for the performance and scalability of automated containment with threat intelligence as the inefficiency of individual object creation for large datasets.
정답:
Explanation:
The 'Preparation' phase sets the foundation for efficient incident response. All options are aspects of preparation, but some directly impact Detection/Analysis and Containment more than others in this specific scenario:
- A: A well-developed playbook with Cortex XDR automation (e.g., playbooks for ransomware containment) directly guides and speeds up response actions, impacting both detection analysis and containment.
- B: Integration of security tools (Cortex XDR, WildFire, AutoFocus) allows for faster threat correlation, automated analysis of suspicious files, and rapid deployment of new protections, directly supporting Detection and Analysis and enabling effective Containment by leveraging shared threat intelligence.
- C: Phishing simulations and awareness training are preventive measures, part of preparation, but they don't directly facilitate technical detection, analysis, or containment once an incident is ongoing.
- D: Clear communication channels and defined roles/responsibilities (who does what, who to inform) are fundamental for coordinating a rapid and effective response, impacting all phases, especially Containment, by ensuring swift decision-making.
- E: Up-to-date inventories and asset classification are crucial for understanding the impact (Detection/Analysis) and prioritizing containment efforts, ensuring the right assets are protected first. Knowing what you have helps you detect anomalies and contain effectively.
정답:
Explanation:
The 'Containment' phase is critical for limiting the scope of an incident.
For a zero-day, simultaneously limiting spread and gathering intelligence is key.
- A: Custom URL filtering (or Security Policies) for the compromised host is a precise network-level containment that still allows forensic data exfiltration to controlled systems.
- B: Cortex XDR isolation is crucial for endpoint containment, preventing lateral movement, and enabling enhanced logging ensures detailed telemetry for post-incident analysis and new IOC generation.
- C: A sinkhole configuration is an advanced containment and intelligence-gathering technique for C2 traffic, allowing the SOC to understand the attacker's capabilities without further compromise.
- D: Pushing a beta patch globally is highly risky and violates standard change management, potentially causing more disruption.
- E: Notifying users immediately and instructing password changes might be part of recovery or communication but is not a primary technical containment step for the zero-day exploit itself.
정답:
Explanation:
The 'Recovery' phase involves restoring affected systems and services.
Option C is key for robust recovery and preventing re- infection. Simply restoring from backup (A) doesn't guarantee the backup itself wasn't compromised or that new malware wasn't introduced during recovery. Using Cortex XDR's post-infection analysis for residual threats and correlating with WildFire verdicts ensures that restored systems are clean from known and potentially new (zero-day) malware, providing a high level of confidence before full reintegration. Blocking all outbound traffic (B) is too restrictive for recovery, and user training is for prevention. Pinging servers (D) is a basic availability check, not a security validation. Implementing a completely new network architecture (E) is an extreme and often impractical step for most recovery scenarios.
정답:
Explanation:
The 'Eradication' phase focuses on removing the root cause of the incident.
Option B is the most precise and effective. Using Cortex XDR's Live Response allows for surgical removal of the malicious process and persistence mechanism (scheduled task) without taking the critical domain controller offline, minimizing business disruption. Deploying a custom IOC exclusion rule ensures that if the script reappears (e.g., from another compromised host), it's immediately identified and blocked. Disabling the DC (A) or re-imaging (C) causes significant disruption and might not be necessary if the exact persistence is known and removed. Sending memory dumps (D) delays eradication, and generic updates (E) are reactive and not specific to the identified threat.
정답:
Explanation:
The 'Containment, Eradication, and Recovery' phase aims to stop the spread, remove the root cause, and restore services. Blocking the sender and deleting the email (B) are immediate containment and eradication steps for an un-opened malicious email. Initiating WildFire analysis is crucial for updating threat intelligence and preventing similar future attacks, aligning with eradication and future prevention. Isolating the endpoint (A) is a containment step, but a network-wide scan might be too broad at this stage without confirmed compromise, and notifying the user to delete is less effective than forced deletion. Reimaging (C) is overkill if the attachment wasn't opened. Forensic analysis (D) is typically part of eradication/post-incident analysis once the immediate threat is contained. Reporting to law enforcement (E) is a post-incident activity, not an immediate containment step.
정답:
Explanation:
The 'Detection and Analysis' phase focuses on determining if an event is an incident, its scope, and nature. While blocking traffic (A) might be a containment step, immediate full packet capture and correlation with DNS Security logs (B) provide crucial data for analysis without prematurely impacting legitimate services, which is essential for accurate incident classification. Isolating the server (C) and notifying leadership (D) are typically 'Containment, Eradication, and Recovery' or 'Post-Incident Activity' steps, and updating antivirus signatures (E) is a general security hygiene practice, not a primary detection and analysis step for a specific observed anomaly.
정답:
Explanation:
The challenge is to go 'beyond what simple scripting or basic ML-based keyword extraction can achieve' and demonstrate 'True AI.' Options A, B, and E describe advanced applications of ML (classification, summarization, correlation), but they primarily focus on processing and presenting information. While valuable, they don't fundamentally change the paradigm of 'understanding' and 'acting' based on complex, evolving intelligence.
Option D describes an AI optimization capability, but not the core transformation of intelligence integration.
Option C represents the pinnacle of AI in this context. It describes the ability of the system to understand (NLLJ), reason (symbolic AI, knowledge graphs), and act autonomously (dynamic policy generation and deployment) based on complex, unstructured threat intelligence. This moves beyond merely processing data to truly comprehending context, relevance, and autonomously adapting defenses, which is a key differentiator of advanced AI from I ML. The system doesn't just extract keywords; it builds a semantic understanding and then reasons about how to apply that understanding to the specific environment.
정답:
Explanation:
Securing highly dynamic, ephemeral containerized environments is exceptionally challenging for traditional and even isolated ML approaches because baselines constantly shift and context is paramount.
Option C highlights a key differentiator of advanced AI: the ability to build and maintain a dynamic 'knowledge graph' or semantic understanding of the entire environment including ephemeral relationships, dependencies, and context across layers (container, host, network, application). This allows for contextual reasoning and risk prioritization, understanding not just 'what' is happening, but 'where' it is happening in the overall architecture and 'why' it might be malicious or benign given the broader context. This holistic, relational understanding and reasoning capability is beyond simple statistical anomaly detection (ML) on isolated data points and is crucial for effective security in such complex, dynamic environments.
Options A, B, D, and E describe valuable ML or automation features, but they don't capture this higher-level, relational intelligence and contextual reasoning unique to more advanced AI applications in this domain.