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EC-COUNCIL 312-49v11 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Linux and Mac Forensics: This domain addresses forensic methodologies for Linux and macOS systems including data collection, memory forensics, log analysis, APFS examination, and platform-specific investigation tools.
Topic 2
  • Computer Forensics in Today's World: This domain covers fundamentals of computer forensics including cybercrime types, investigation procedures, digital evidence handling, forensic readiness, investigator roles and responsibilities, industry standards, and legal compliance requirements.
Topic 3
  • Malware Forensics: This domain addresses malware investigation including controlled lab setup, static analysis, system and network behavior analysis, suspicious document examination, and ransomware investigation techniques.
Topic 4
  • Computer Forensics Investigation Process: This domain addresses the structured investigation phases including first response procedures, lab setup, evidence preservation, data acquisition, case analysis, documentation, reporting, and expert witness testimony.
Topic 5
  • Mobile Forensics: This domain covers Android and iOS forensics including device architecture, forensics processes, cellular data investigation, file system acquisition, lock bypassing, rooting
  • jailbreaking, and mobile application analysis.
Topic 6
  • Windows Forensics: This domain covers Windows-specific investigation techniques including volatile and non-volatile data collection, memory and registry analysis, web browser forensics, metadata examination, and analysis of Windows artifacts like ShellBags, LNK files, and event logs.
Topic 7
  • Cloud Forensics: This domain covers cloud platform forensics (AWS, Azure, Google Cloud) including data storage, logging, forensic acquisition of virtual machines, and investigation of cloud security incidents.
Topic 8
  • Email and Social Media Forensics: This domain addresses email crime investigation including message analysis, U.S. email laws, social media activity tracking, footage extraction, and social network graph analysis.
Topic 9
  • Dark Web Forensics: This domain addresses dark web investigation focusing on Tor browser artifact identification, memory dump analysis, and extracting evidence of dark web activities.
Topic 10
  • Network Forensics: This domain covers network incident investigation through traffic and log analysis, event correlation, indicators of compromise identification, SIEM usage, and wireless network attack detection and examination.
Topic 11
  • Understanding Hard Disks and File Systems: This domain covers storage media characteristics, disk logical structures, operating system boot processes (Windows, Linux, macOS), file systems analysis, encoding standards, and examination of common file formats.

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EC-COUNCIL Computer Hacking Forensic Investigator (CHFI-v11) Sample Questions (Q277-Q282):

NEW QUESTION # 277
Lucas, a forensic investigator, is working on an investigation involving a compromised hard drive. To analyze the disk image and extract relevant forensic data, he decides to use a tool that integrates the powerful capabilities of Sleuth Kit with Python scripting. Lucas wants to automate the process of analyzing disk structures, file systems, and file recovery using Python scripts. Which of the following tools can help Lucas leverage Sleuth Kit's capabilities while using Python to perform these analysis tasks efficiently?

Answer: C

Explanation:
According to CHFI v11 objectives underComputer Forensics FundamentalsandDigital Forensics using Python, investigators are encouraged to automate forensic analysis tasks to improve efficiency, accuracy, and repeatability. The Sleuth Kit (TSK) is a widely used open-source forensic toolkit for analyzing disk images, file systems, and recovering deleted files. To extend these capabilities using Python, CHFI v11 highlights the use of Python bindings specifically designed for forensic purposes.
PyTSK (also known as pytsk3) is the official Python binding for The Sleuth Kit. It allows forensic investigators to programmatically access disk images, partitions, file systems, directories, and file metadata directly from Python scripts. This enables automation of tasks such as file enumeration, timeline creation, deleted file recovery, and artifact extraction-core activities in disk and file system forensics.
The other options are not suitable in this context. NumPy is designed for numerical computation, PyTorch is used for machine learning, and PySpark is intended for big data processing. None of these tools integrate with Sleuth Kit or provide native disk forensic analysis capabilities. Therefore, PyTSK is the correct and CHFI- aligned choice for Python-based Sleuth Kit forensic automation.


NEW QUESTION # 278
During a corporate cyber espionage case in Austin, Texas, forensic investigators analyze how the company's storage systems were accessed during exfiltration. They discover that attackers mapped a shared folder accessible via SMB protocol from multiple departments, while critical databases remained on a separate high-speed Fibre Channel storage fabric. Which storage model does the shared folder system represent?

Answer: A

Explanation:
Network-Attached Storage provides file-level shared storage over a network using protocols such as SMB or NFS. A shared folder mapped by multiple departments through SMB represents NAS rather than block-level storage such as a Fibre Channel SAN.


NEW QUESTION # 279
When performing a forensics analysis, what device is used to prevent the system from recording data on an evidence disk?

Answer: D


NEW QUESTION # 280
During a post-incident investigation at a retail technology company, forensic analysts must reconstruct a timeline of unauthorized modifications made to cloud resources across multiple AWS accounts. The investigation requires visibility into control-plane activity so analysts can attribute actions to specific identities and understand how configuration changes were initiated and propagated throughout the environment. How should investigators obtain this account-wide record of management activity to support timeline reconstruction?

Answer: B

Explanation:
The correct answer is D because AWS CloudTrail is the AWS service that records management activity across an account, including actions taken through the AWS Management Console, CLI, SDKs, and APIs.
AWS documentation explains that CloudTrail provides a history of account activity and captures management events, which is exactly what investigators need when reconstructing who changed cloud resources, when those changes occurred, and how they were initiated. That makes it the key source for control-plane timeline analysis. Amazon S3 Server Access Logging is limited to S3 request logging and does not provide broad account-wide management visibility. The AWS CLI is a tool for interacting with AWS, not the forensic record itself. Amazon CloudWatch can collect metrics and logs, but the question specifically asks for the authoritative account-wide record of management actions. CHFI v11 includes cloud forensics and AWS evidence sources, so candidates are expected to distinguish platform activity logs from service-specific or tooling components. For unauthorized modifications across AWS accounts, CloudTrail is the primary source for identity-linked management event reconstruction.


NEW QUESTION # 281
A multinational technology corporation believes a former executive may have gained unauthorized access to private company information. The executive is being investigated for possibly sending private data after switching from an Android to an iOS smartphone. The forensic investigation team has to carefully review the digital data in order to support their allegations.
Which of the following claims about the file systems of iOS and Android is most true in light of this scenario?

Answer: D

Explanation:
Option B is the most accurate answer because CHFI v11 explicitly includes Android and iOS file systems , along with Android and iOS forensic analysis , as core mobile forensics topics. In practical CHFI study terms, Android devices are commonly associated with Ext4 in many forensic contexts, while iOS devices use APFS , which is also specifically reflected in the blueprint through APFS file system analysis .
Option A is weaker because APFS is not best summarized simply as a traditional journaling filesystem in the same way older filesystems often are. Option C is incorrect because Android forensic access is not automatically simple or direct; it often still requires appropriate forensic tools, permissions, or acquisition methods. Option D is also incorrect because FileVault is associated with Apple's macOS environment rather than being the correct way to describe iOS file-system security in this question.
Since the question asks for the statement most true about Android and iOS file systems, the answer that best aligns with CHFI's mobile operating system and file system objectives is that Android relies on Ext4 while iOS uses APFS .


NEW QUESTION # 282
......

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