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| Section | Objectives |
|---|---|
| Application Development Environment | - Development tools
|
| Data Modeling and Server Development | - Entity and business logic development
|
| Customization and Extension | - Custom development
|
| Testing and Troubleshooting | - Application validation
|
| Curam Platform Architecture | - Application architecture
|
| Client Development | - User interface development
|
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NEW QUESTION # 11
Based on recent alerts, a security analyst thinks a web application server was compromised. The analyst reviews the following server output:
Which of the following best describes what has occurred?
Answer: D
Explanation:
The server output indicates an initiated unauthorized session , which is the most significant security condition among the available answers. During compromise analysis, login/session data must be evaluated for unexpected users, remote origins, terminals, login times, active processes, and activity inconsistent with the server's expected operational baseline.
Linux session utilities provide precisely this type of evidence. The who utility reports users who are currently logged into a system, while w provides additional information such as the login name, terminal, remote host, login time, idle time, and currently associated process. An unexpected active session on a web application server-particularly one inconsistent with normal administrative activity-is therefore a material indicator of possible unauthorized access.
"Too many users" would require evidence that session volume itself exceeded an established threshold. High resource consumption would instead require CPU, memory, load-average, or process-utilization evidence.
Abnormal idle times might warrant investigation but do not independently establish compromise.
The analyst should treat the unauthorized session as an investigative pivot and correlate it with authentication logs, source addresses, process execution, privilege changes, and network connections.
Study Guide Reference: Incident Response and Management # Analysis # Host-Based Evidence # User Sessions # Authentication Activity # Unauthorized Access # Event Correlation.
NEW QUESTION # 12
Hotspot Question
A systems administrator is reviewing the output of a vulnerability scan.
INSTRUCTIONS
Review the information in each tab.
Based on the organization's environment architecture and remediation standards, select the server to be patched within 14 days and select the appropriate technique and mitigation.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
Answer:
Explanation:
Explanation:
192.168.60.90 is an operations server with a CVSS score of 8.1, requiring remediation within 14 calendar days. Restricting the server to modern cipher suites directly mitigates the identified TLS downgrade vulnerability.
NEW QUESTION # 13
A security analyst reruns infrastructure as code (IaC) to tear down and rebuild a new environment after a ransomware attack. Which of the following describes this phase?
Answer: C
Explanation:
Rebuilding the environment from trusted IaC configurations restores systems and services to normal operation after the ransomware attack.
NEW QUESTION # 14
An analyst uses an AI platform to help correlate events. The AI output contains events that did not happen.
This results in inaccurate correlations.
Which of the following best describes what has occurred?
Answer: A
Explanation:
The scenario describes an AI hallucination , commonly termed confabulation in formal AI risk-management literature. The defining characteristic is that the model produces information that appears plausible but is factually incorrect or unsupported. Here, the AI system introduces events that never occurred, contaminating the event-correlation process and potentially causing analysts to reach incorrect conclusions.
NIST's Generative AI Profile identifies confabulation as the production of confidently stated but erroneous or false content and treats it as an AI risk that requires verification and monitoring. NIST cybersecurity guidance also recognizes hallucination and confabulation as risks to information accuracy when AI is incorporated into cybersecurity workflows.
Data exposure would involve unauthorized disclosure of confidential or sensitive information. A malicious prompt involves intentionally crafted input designed to influence model behavior or bypass restrictions.
Model poisoning occurs when an adversary manipulates training or model-related data to corrupt the system's behavior. None of these conditions is required in the scenario; the critical evidence is fabrication of nonexistent events.
Security analysts therefore must treat AI-generated correlation as analytical assistance rather than unquestioned evidence and validate important conclusions against authoritative logs and telemetry.
Study Guide Reference: Security Operations # Artificial Intelligence # AI Risks # Hallucinations # Data Exposure # Malicious Prompts # Model Poisoning # Human Validation.
NEW QUESTION # 15
A security operations center (SOC) analyst investigates the results of a password spray test conducted by the vulnerability management team. The analyst must:
- Identify Linux systems that have successful and unsuccessful logins
with username "User1".
- Create an output report named "linux-events" of all the events to a
flat file.
The analyst issues the following console command:
ls /var/log/
The shortened output of the command is below:
Which of the following commands should the analyst use to meet the report output requirements?
Answer: C
Explanation:
/var/log/auth.log contains Linux authentication events, including successful and failed login attempts. grep selects entries for User1, and > writes them to the required flat file.
NEW QUESTION # 16
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