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| Section | Objectives |
|---|---|
| Topic 1: Automation and SOAR Processes | - Case management and enrichment - Playbook design and automation logic |
| Topic 2: Threat Hunting and Analytics | - Hypothesis-driven threat hunting - Log analysis and behavioral detection |
| Topic 3: Threat Detection and Incident Response | - Incident response lifecycle - Malware analysis fundamentals - Threat intelligence and analysis |
| Topic 4: Security Operations Fundamentals | - Security monitoring and alert triage concepts - SOC workflows and operating models |
| Topic 5: Palo Alto Networks Security Operations Platforms | - Security data ingestion and correlation - Cortex XDR detection and response - Cortex XSOAR automation and orchestration concepts |
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NEW QUESTION # 62
A high-profile executive's workstation shows suspicious activity detected by Cortex XDR's User and Entity Behavior Analytics (UEBA). The activity includes: 1) Login from an unusual geolocation for the user, 2) Accessing sensitive files on a SharePoint site the user rarely interacts with, and 3) Attempting to download a large amount of data to a personal cloud storage service. No direct malware alerts were triggered. Which of the following statements accurately describes how Cortex XDR's UEBA component synthesizes these disparate 'events of interest' to generate a high-fidelity alert, and what underlying principle makes this possible?
Answer: B
Explanation:
Cortex XDRs UEBA capability is fundamentally driven by machine learning, specifically unsupervised learning, to build dynamic baselines of user and entity behavior. It profiles what is 'normal' for a given user (login patterns, accessed resources, data transfer habits, etc.). When observed activities (unusual geolocation, accessing rarely used sensitive files, exfiltrating data to personal cloud) deviate significantly from this established baseline, they are identified as anomalies. The system then correlates these individual anomalies, aggregates their risk scores, and contextualizes them to generate a high-fidelity alert for potential account compromise or insider threat. This approach is superior to static rules or threat intelligence alone as it adapts to dynamic environments and detects novel threats without prior knowledge of specific attack patterns.
NEW QUESTION # 63
What is a primary responsibility of an incident responder in a SOC?
Answer: B
Explanation:
An incident responder's primary responsibility in a SOC is to mitigate incidents that have been escalated, containing and remediating threats.
NEW QUESTION # 64
A custom application running on a Linux server is suspected of being compromised. The threat actor is believed to be leveraging a zero-day vulnerability in the application to execute arbitrary code and establish a reverse shell. Cortex XDR agents are deployed on this Linux server. You, as a SOC analyst, need to identify the exact process that initiated the reverse shell, its parent process, and any outbound network connections to suspicious external IPs. Which XDR Query Language (XQL) query against Cortex Data Lake would be most effective for this specific investigation, assuming the reverse shell typically connects to port 443 on an unprivileged user's behalf from an unusual location?
Answer: B
Explanation:
To identify the reverse shell's process, its parent, and outbound connections, we need to correlate network connection events with process execution events. Option B starts by filtering for relevant network connections (outbound on port 443), then joins this with process execution data using the process ID. This allows for identifying the process responsible for the network connection and its parent , process_events.actor_process_command_line'), and the destination IP. Option A has an incorrect join condition; it tries to filter for bash/sh first and then join based on process_id, which might miss other reverse shell binaries. Options C, D, and E are irrelevant to the specific goal of tracing a reverse shell's process and network activity.
NEW QUESTION # 65
Which two types of tasks are supported in Cortex XSIAM playbooks? (Choose two.)
Answer: B,C
Explanation:
Cortex XSIAM playbooks utilize a structured workflow to automate SOC processes. The task types define how the logic flows through the playbook:
* Sub-playbook (A): This allows an analyst to call another existing playbook as a single step within a larger workflow. This is crucial for modularity, such as having a standard "IP Enrichment" sub- playbook that is used inside multiple different parent playbooks (e.g., Phishing and Brute Force).
* Conditional (C): These are "decision" nodes (often visualized as Yes/No or multiple-choice branches).
They evaluate data from previous steps to determine which path the playbook should take next.
* Data Collection (D): While "Data Collection" tasks (like surveys/forms) are supported in XSOAR , the core task types in the native XSIAM automation engine emphasize Standard , Conditional , and Sub- playbook tasks.
* Note on Scripting: While you can run an automation script (Python) as a "Standard" task, "Script creation" is a development activity, not a functional task type within an active playbook.
NEW QUESTION # 66
A Security Operations Center (SOC) analyst is performing threat hunting based on an observed surge in outbound DNS requests to unusual top-level domains (TLDs) from internal hosts, specifically from a segment traditionally used by financial analysts. These TLDs are not typically seen in legitimate business traffic. The threat intelligence team has recently reported an increase in Cobalt Strike beaconing activity leveraging DNS over HTTPS (DOH) to obscure C2 communications. Which of the following Splunk Search Processing Language (SPL) queries would be most effective in identifying suspicious DNS-related indicators of compromise (IOCs) aligned with this threat, assuming 'pan_logS is the relevant sourcetype for Palo Alto Networks firewall logs?
Answer: E
Explanation:
The scenario specifically mentions 'DNS over HTTPS (DOH)' and 'unusual TLDs' and 'Cobalt Strike beaconing'. Option C directly addresses DOH by filtering for (common for HTTPS) and then correlates it with or , which are strong indicators of DOH traffic attempting to bypass traditional DNS monitoring. While other options might identify general DNS anomalies, Option C is the most targeted and effective for the described threat given the specific indicators. Option B is good for unusual TLDs but misses the DOH aspect and relies on a pre-defined lookup. Option A is too broad and only looks for specific TLDs rather than anomalies. Option D looks for non-standard DNS ports, but DOH uses 443. Option E relies on an undefined macro.
NEW QUESTION # 67
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