Free ECCouncil 312-85 Practice Exam with Questions & Answers | Set: 3

When attackers or analysts search job postings on online job portals, they often uncover technical details inadvertently shared by organizations.

Job listings frequently mention:

Hardware and software used (e.g., “experience with Cisco firewalls, Windows Server 2019”).

Network details and tools (e.g., “knowledge of LAN/WAN, AWS, Azure”).

Security technologies (e.g., “SIEM tools like Splunk or QRadar”).

This information can help analysts identify the technological footprint of the company, which is valuable during threat profiling or reconnaissance.

Why the Other Options Are Incorrect:

B. Top-level domains and subdomains: Obtained through DNS enumeration tools, not job sites.

C. Open ports and services: Found through active scanning tools like Nmap, not via job postings.

Conclusion:

Flora can obtain hardware, software, and network-related information from online job listings.

Final Answer: A. Hardware and software information, network-related information, and technologies used by the company

Explanation Reference (Based on CTIA Study Concepts):

CTIA recognizes online job sites as OSINT sources that can reveal technical environment details about organizations.

In this scenario, the robot learns through trial and error, receiving positive or negative feedback to improve its actions over time. This describes Reinforcement Learning (RL).

Reinforcement Learning is a machine learning approach where an agent interacts with an environment to achieve a goal. It learns optimal behavior by taking actions, receiving feedback (rewards or penalties), and refining its strategy to maximize cumulative rewards.

This method is widely used in robotics, game theory, and autonomous systems where explicit labeled data is not available, but performance can be measured by rewards.

Why the Other Options Are Incorrect:

Unsupervised learning: Involves finding patterns or clusters in unlabeled data without feedback.

Semi-supervised learning: Combines a small set of labeled data with a large amount of unlabeled data.

Supervised learning: Requires labeled datasets to train models on known input-output pairs.

Conclusion:

The robot uses Reinforcement Learning to optimize its performance based on feedback loops.

Final Answer: C. Reinforcement learning

Explanation Reference (Based on CTIA Study Concepts):

Under the CTIA topic “Machine Learning in Threat Intelligence,” reinforcement learning is defined as feedback-driven learning through reward and punishment signals.

Tactical threat intelligence analysis focuses on the immediate, technical indicators of threats, such as the tactics, techniques, and procedures (TTPs) used by adversaries, their communication channels, the tools and software they utilize, and their strategies for evading forensic analysis. This type of analysis is crucial for operational defenses and is used by security teams to adjust their defenses against current threats. Since John successfully extracted information related to the adversaries' modus operandi, tools, communication channels, and evasion strategies, he is performing tactical threat intelligence analysis. This differs from strategic and operational threat intelligence, which focus on broader trends and specific operations, respectively, and from technical threat intelligence, which deals with technical indicators like malware signatures and IPs.

In the Analysis of Competing Hypotheses (ACH) process, the stage where Mr. Bob is applying analysis to reject hypotheses and select the most likely one based on listed evidence, followed by preparing a matrix with screened hypotheses and evidence, is known as the 'Refinement' stage. This stage involves refining the list of hypotheses by systematically evaluating the evidence against each hypothesis, leading to the rejection of inconsistent hypotheses and the strengthening of the most plausible ones. The preparation of a matrix helps visualize the relationship between each hypothesis and the available evidence, facilitating a more objective and structured analysis.

Threat Grid is a threat intelligence and analysis platform that offers advanced capabilities for automatic data collection, filtering, and analysis. It is designed to help organizations convert raw threat data into meaningful, actionable intelligence. By employing advanced analytics and machine learning, Threat Grid can reduce noise from large data sets, helping to eliminate misrepresentations and enhance the quality of the threat intelligence. This makes it an ideal choice for Tim, who is looking to address the challenges of converting raw data into contextual information and managing the noise from massive data collections.

In the scenario described, where attackers have penetrated the network and are staging data for exfiltration, Jim should focus on monitoring network traffic for signs of malicious file transfers, implement file integrity monitoring, and scrutinize event logs. This approach is crucial for detecting unusual activity that could indicate data staging, such as large volumes of data being moved to uncommon locations, sudden changes in file integrity, or suspicious entries in event logs. Early detection of these indicators can help in identifying the staging activity before the data is exfiltrated from the network.