Expected ITS-110 Exam Topics, as suggested by CertNexus :

• Module 1: Securing IoT Portals: This topic focuses on protecting web, cloud, and mobile interfaces used in IoT systems. It gives Internet of Things Security Practitioners knowledge about identifying threats and implementing measures to secure these entry points from vulnerabilities and attacks.
• Module 2: Implementing Authentication, Authorization, and Accounting: Internet of Things Security Practitioners must secure authentication and authorization schemes and ensure accurate accounting. The ITS-110 exam tests skills in preventing unauthorized access and maintaining secure identity management within IoT systems.
• Module 3: Securing Network Services: This topic involves identifying and countering threats to network services. It emphasizes securing network communications and services to prevent exploitation and ensure the integrity of IoT network infrastructure.
• Module 4: Securing Data: Focusing on data protection, this topic of the CertNexus Certified Internet of Things Security Practitioner exam covers identifying and addressing threats to unsecured data. It tests the ability to implement countermeasures to safeguard data confidentiality and integrity in IoT environments.
• Module 5: Addressing Privacy Concerns: Internet of Things Security Practitioners need to identify privacy threats and implement measures to protect personal data. This topic ensures IoT practitioners can maintain user privacy and comply with relevant privacy regulations.
• Module 6: Securing Software/Firmware: It addresses threats to software and firmware in IoT systems. It assesses the ability of IoT Security Practitioner to identify vulnerabilities and apply security measures to ensure software and firmware remain secure against exploits.
• Module 7: Enhancing Physical Security: Focusing on physical security, this topic of the CertNexus ITS-110 exam covers identifying and mitigating threats related to poor physical security.

Expected CFR-410 Exam Topics, as suggested by CertNexus :

• Module 1: Implement system security measures in accordance with established procedures/ Determine tactics, techniques, and procedures (TTPs) of intrusion sets
• Module 2: Protect identity management and access control within the organization/ Employ approved defense-in-depth principles and practices
• Module 3: Develop and implement cybersecurity independent audit processes/ Analyze and report system security posture trends
• Module 4: Establish relationships between internal teams and external groups like law enforcement agencies and vendors/ Identify and evaluate vulnerabilities and threat actors
• Module 5: Provide advice and input for disaster recovery, contingency/ Implement specific cybersecurity countermeasures for systems and applications
• Module 6: Perform analysis of log files from various sources to identify possible threats to network security/ Protect organizational resources through security updates
• Module 7: Identify and conduct vulnerability assessment processes/ Identify applicable compliance, standards, frameworks, and best practices for privacy
• Module 8: Analyze common indicators of potential compromise, anomalies, and patterns/ Review forensic images and other data sources for recovery of potentially relevant information
• Module 9: Determine the extent of threats and recommend courses of action or countermeasures to mitigate risks/ Correlate incident data and create reports
• Module 10: Identify applicable compliance, standards, frameworks, and best practices for security/ Execute the incident response process
• Module 11: Identify factors that affect the tasking, collection, processing, exploitation/ Implement recovery planning processes and procedures to restore systems and assets affected by cybersecurity incidents

Expected AIP-210 Exam Topics, as suggested by CertNexus :

• Module 1: Identify potential ethical concerns/ Analyze machine learning system use cases
• Module 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
• Module 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
• Module 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
• Module 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
• Module 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
• Module 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow