CertNexus CET study plan

Define the CIA triad and map each leg to at least two concrete security controls used in enterprise environments.

Distinguish authentication, authorization, and accounting, and give one real-world example of each failing.

Classify the major threat categories (malware, social engineering, insider, denial of service) and match each to a plausible mitigation.

Describe how IoT device constraints (limited CPU, memory, power, and patching ability) change the risk profile compared with general-purpose computing.

Explain the role of the IoT reference model layers and identify which layer a given control protects.

Compare symmetric and asymmetric cryptography and state when each is appropriate for constrained IoT devices.

Explain hashing, digital signatures, and certificates and how they combine to provide integrity and non-repudiation.

Identify common IoT attack surfaces: default credentials, unencrypted protocols, insecure update channels, and exposed management interfaces.

Outline a secure device onboarding process from manufacturing through network enrollment.

Apply network segmentation and least-privilege principles to isolate an IoT device fleet from core systems.

Compare IoT connectivity options (Wi-Fi, Bluetooth Low Energy, Zigbee, LoRaWAN, cellular) on range, bandwidth, power, and typical use case.

Describe edge versus cloud processing and justify a placement decision for a latency-sensitive workload.

Define core AI concepts: supervised, unsupervised, and reinforcement learning, and give one realistic application of each.

Explain how training data, features, and labels relate, and identify what makes a dataset unsuitable for training.

Recognize how AI is embedded in IoT systems for anomaly detection and predictive maintenance.

Describe the stages of a data science lifecycle from problem framing through deployment and monitoring.

Distinguish classification, regression, clustering, and dimensionality reduction and select the right type for a stated problem.

Explain overfitting and underfitting and name at least three techniques to reduce each.

Interpret common evaluation metrics (accuracy, precision, recall, F1, RMSE) and explain why accuracy alone can mislead on imbalanced data.

Identify sources of bias in data collection and explain how they propagate into model outputs.

Explain data wrangling and feature engineering steps and demonstrate why data quality dominates model quality.

Distinguish structured, semi-structured, and unstructured data and describe storage and processing implications for each.

Summarize the main data privacy regulations and principles (consent, purpose limitation, minimization, retention) without relying on jurisdiction-specific detail you cannot verify.

Apply the concepts of anonymization, pseudonymization, and de-identification and state their limits.

Identify ethical risks in a data project using a structured framework such as fairness, accountability, transparency, and explainability.

Explain how AI systems introduce new security concerns: data poisoning, adversarial inputs, model theft, and prompt injection.

Describe the principle of privacy by design and apply it to a system that collects sensor data about people.

Analyze an automated decision system for disparate impact and propose a fairness remediation.

Map data ethics obligations to security controls such as access logging, encryption, and retention enforcement.

Evaluate the tension between model transparency and proprietary protection and argue a defensible position.

Build a cross-domain concept map linking each of the five domains to at least two others with a concrete example.

Answer a timed set of mixed-domain practice questions and log every miss with the underlying concept, not just the answer.

Re-teach your three weakest concepts in writing as if explaining them to a non-technical colleague.

Identify the two domains where your practice accuracy is lowest and rank the specific subtopics responsible.

Close every gap identified in the week 7 error log by reviewing and re-testing each weak subtopic.

Recite from memory the key definitions and distinctions for each domain without notes.

Complete a final full-length timed practice run under exam-like conditions.

Finalize a one-page personal reference sheet of facts you repeatedly forgot.