Applied AI cert-prep add-on

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Exam Prep Add-On

Convert AI Security Engineering into a CertNexus CAIP ramp for the vendor-neutral AI practitioner credential.

$147 add-on50h additional studyTarget exam: CertNexus Certified Artificial Intelligence Practitioner (CAIP)Exam fee: $498

What the add-on ships

++Domain reviews aligned to the official CertNexus CAIP exam outline
++Three full-length mock exams in CertNexus's question format
++Worked examples of AI lifecycle decisions under adversarial-input pressure
++AI ethics, fairness, and responsible-AI scenarios in the format CAIP ships
++Direct link to the official CertNexus CAIP exam page as the canonical reference

Built on the parent course

++AI Security Engineering 16-module curriculum covering threat modeling, prompt injection, adversarial examples, AI red teaming, AI security infrastructure
++Lifecycle defense patterns from the parent course that map directly to CAIP scenarios

Parent course: ai security engineering

Best for

++AI Engineers and ML Engineers in regulated industries that prefer ANAB ISO 17024 accredited credentials over vendor-specific ones
++AI Security Engineers consolidating security work into a recognized vendor-neutral credential
++Career changers entering Applied AI who want a credential not tied to a single cloud provider

Practice surface

++Three full-length mock exams in CertNexus's format
++AI lifecycle decision worked examples under adversarial input
++Targeted weak-area question drilling per CAIP exam objective

Buy the add-on

$147 on top of the ai security engineering parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

CertNexus Certified Artificial Intelligence Practitioner (CAIP) is a vendor-neutral AI practitioner credential accredited under ANSI ISO 17024. The exam covers AI lifecycle, ML and deep learning fundamentals, computer vision, NLP, generative AI, ethics, and operationalization. 80 questions in 90 minutes.

Domain reviews

AI Fundamentals and Lifecycle

Vocabulary, AI lifecycle, machine learning categories.

++AI lifecycle: problem framing, data preparation, modeling, evaluation, deployment, monitoring
++ML categories: supervised, unsupervised, reinforcement, self-supervised
++Classical algorithms: linear models, tree-based, k-NN, k-means, PCA
++Deep learning: neural network basics, backpropagation, activation functions, loss functions
++Training-test-validation splits, cross-validation, train-test contamination

Primary sources:

Computer Vision and NLP

Specialized AI domains: vision and language.

++Computer vision: CNNs, image classification, object detection, segmentation
++Vision transformers (ViT)
++NLP: tokenization, embeddings, transformers, attention
++Pre-trained models: BERT, GPT, T5, Llama families
++Common tasks: classification, NER, summarization, translation, question answering

Primary sources:

Generative AI

Modern generative models and applications.

++Large language models: pretraining, instruction tuning, RLHF
++Diffusion models for image generation
++Prompt engineering and RAG
++Fine-tuning techniques: full, LoRA, PEFT
++Multimodal models (text + image + audio)

Primary sources:

AI Ethics and Responsible AI

Fairness, transparency, accountability.

++Bias sources: data bias, model bias, deployment bias
++Fairness criteria: demographic parity, equal opportunity, equalized odds
++Explainability: SHAP, LIME, attention visualization
++Privacy: differential privacy, federated learning
++Governance: NIST AI RMF, model cards, AI impact assessments

Primary sources:

Operationalization and MLOps

Deploying and operating AI in production.

++MLOps lifecycle: experiment tracking, model registry, deployment, monitoring
++Drift detection: data drift, concept drift, prediction drift
++A/B testing and progressive deployment
++Inference patterns: batch, real-time, streaming
++Cost and latency optimization: quantization, distillation, batching

Primary sources:

MLflow documentation

Practice scenarios (6)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 6 · AI Fundamentals and Lifecyclefree preview

A practitioner reports their model has 99% accuracy on the training set but only 60% on the held-out test set. Which phenomenon best explains this gap?

A. Underfitting
B. Overfitting
C. Data leakage
D. Gradient explosion

Answer: B

A large gap between training and held-out test accuracy is the canonical signal of overfitting: the model has memorized the training data rather than learning the underlying pattern. Underfitting would show low accuracy on both. Data leakage would show suspiciously high test accuracy. Gradient explosion is a training-time numerical issue.

Reference: Goodfellow et al. Chapter 5

Unlock the rest

5 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify the domain quickly. CAIP questions cover a broad surface; the right answer is usually the canonical AI-textbook term for the scenario.
++Watch for fairness-criterion swaps (demographic parity vs equal opportunity vs equalized odds).
++On generative-AI questions, look for the named pattern (RAG, fine-tuning, prompt engineering) that matches the scenario constraints.
++On MLOps questions, distinguish data drift from concept drift from prediction drift. The exam tests these distinctions.
++Pace at roughly 65 seconds per question. 90 minutes for 80 questions is moderate.

Additional resources

Sources

Official CertNexus Certified Artificial Intelligence Practitioner (CAIP) exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Domain reviews aligned to the official CertNexus CAIP exam outline

++Three full-length mock exams in CertNexus's question format

++Worked examples of AI lifecycle decisions under adversarial-input pressure

++AI ethics, fairness, and responsible-AI scenarios in the format CAIP ships

++Direct link to the official CertNexus CAIP exam page as the canonical reference

Best for

++AI Engineers and ML Engineers in regulated industries that prefer ANAB ISO 17024 accredited credentials over vendor-specific ones

++AI Security Engineers consolidating security work into a recognized vendor-neutral credential

++Career changers entering Applied AI who want a credential not tied to a single cloud provider

Exam summary

Domain reviews

AI Fundamentals and Lifecycle

Vocabulary, AI lifecycle, machine learning categories.

++AI lifecycle: problem framing, data preparation, modeling, evaluation, deployment, monitoring
++ML categories: supervised, unsupervised, reinforcement, self-supervised
++Classical algorithms: linear models, tree-based, k-NN, k-means, PCA
++Deep learning: neural network basics, backpropagation, activation functions, loss functions
++Training-test-validation splits, cross-validation, train-test contamination

Primary sources:

Computer Vision and NLP

Specialized AI domains: vision and language.

++Computer vision: CNNs, image classification, object detection, segmentation
++Vision transformers (ViT)
++NLP: tokenization, embeddings, transformers, attention
++Pre-trained models: BERT, GPT, T5, Llama families
++Common tasks: classification, NER, summarization, translation, question answering

Primary sources:

Generative AI

Modern generative models and applications.

++Large language models: pretraining, instruction tuning, RLHF
++Diffusion models for image generation
++Prompt engineering and RAG
++Fine-tuning techniques: full, LoRA, PEFT
++Multimodal models (text + image + audio)

Primary sources:

AI Ethics and Responsible AI

Fairness, transparency, accountability.

++Bias sources: data bias, model bias, deployment bias
++Fairness criteria: demographic parity, equal opportunity, equalized odds
++Explainability: SHAP, LIME, attention visualization
++Privacy: differential privacy, federated learning
++Governance: NIST AI RMF, model cards, AI impact assessments

Primary sources:

Operationalization and MLOps

Deploying and operating AI in production.

++MLOps lifecycle: experiment tracking, model registry, deployment, monitoring
++Drift detection: data drift, concept drift, prediction drift
++A/B testing and progressive deployment
++Inference patterns: batch, real-time, streaming
++Cost and latency optimization: quantization, distillation, batching

Primary sources:

MLflow documentation

Practice scenarios (6)

Preview scenario 1 of 6 · AI Fundamentals and Lifecyclefree preview

A practitioner reports their model has 99% accuracy on the training set but only 60% on the held-out test set. Which phenomenon best explains this gap?

A. Underfitting
B. Overfitting
C. Data leakage
D. Gradient explosion

Answer: B

Reference: Goodfellow et al. Chapter 5

Unlock the rest

5 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Identify the domain quickly. CAIP questions cover a broad surface; the right answer is usually the canonical AI-textbook term for the scenario.

++Watch for fairness-criterion swaps (demographic parity vs equal opportunity vs equalized odds).

++On generative-AI questions, look for the named pattern (RAG, fine-tuning, prompt engineering) that matches the scenario constraints.

++On MLOps questions, distinguish data drift from concept drift from prediction drift. The exam tests these distinctions.

++Pace at roughly 65 seconds per question. 90 minutes for 80 questions is moderate.