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ISTQB® CT AI Testing

Upgrade your testing workflow by applying AI-driven tools for smarter test design and faster analysis.

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INTRODUCTION

Software Reliability Weakens When Bias Remains Unchecked

Apply AI-supported testing tools to generate optimized test suites, detect anomalies, and accelerate defect analysis. By mastering these practices, you streamline testing cycles, strengthen model reliability, and transform AI from a potential risk into a strategic advantage for your organization.

Why Choose the ISTQB® CT AI Testing

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Upcoming Schedules

Reserve your spot in advance and choose the schedule that best fits your availability

COURSE OVERVIEW

What You’ll Learn in the ISTQB® CT AI Testing

DAY 1:

1. Introduction to Artificial Intelligence
  • AI-Based and Conventional Systems
  • Narrow AI, General AI, and Super AI
  • Different Types of AI Technologies
  • Generative AI
  • Hardware for ML Systems
  • Development and Hosting Options for AI Models
  • ML Development Frameworks
  • Regulations, Standards, and AI-Based Systems
  • Quality Characteristics of AI-Based Systems
  • Challenges of Safety-Related AI-Based Systems
  • Acceptance Criteria for AI Quality Characteristics
  • Different Forms of Machine Learning
  • Machine Learning Workflow
  • Hands-on Exercise: Create a Machine Learning Model
  • Pretrained Models, Fine-Tuning, and Retrieval-Augmented Generation
  • Activities in Data Preparation
  • Hands-on Exercise: Data Preparation for an ML Model
  • Training, Validation, and Test Datasets

DAY 2:

  • Calculation of ML Functional Performance Metrics
  • Hands-on Exercise: Evaluate an ML Model
  • Hands-on Exercise: Impact of Models and Dataset
  • Structure and Working of a Deep Neural Network
  • Hands-on Exercise: Implementation of a Perceptron
  • Coverage Measures for Neural Networks
  • Locked and Adaptive AI-Based Systems
  • Rationale for a Statistical Approach
  • Test Oracles for AI-Based Systems
  • Testing Generative AI
  • Red Teaming for GenAI Systems
  • Hands-on Exercise: Exploratory Testing of an LLM
  • Test Levels for Machine Learning Systems
  • Risk-Based Testing of Machine Learning Systems
  • Introduction; input data risks and mitigations; testing for bias

DAY 3:

  • Input Data Risks and Mitigations
  • Testing for Bias
  • Data Pipeline Testing
  • Testing for Data Representativeness
  • Dataset Constraint Testing
  • Label Correctness Testing
  • Hands-on Exercise: Input Data Testing for ML Datasets
  • ML Model Risks and Mitigations
  • ML Model Documentation Review
  • ML Functional Performance Testing
  • Adversarial Testing
  • Metamorphic Testing
  • Hands-on Exercise: Apply Metamorphic Testing
  • Drift Testing
  • Overfitting and Underfitting
  • A/B Testing
  • Back-to-Back Testing
  • ML Development Risks and Mitigations
  • ML System Deployment Testing

COURSE OVERVIEW

What You’ll Learn in the ISTQB® CT AI Testing

DAY 1:
Foundations of AI, ML & System Behaviour

1. Introduction to AI, ML & Deep Learning
  • Understand what AI means and why “AI Effect” matters.
  • Distinguish Narrow, General, and Super AI.
  • Compare AI systems with conventional rule-based systems.
  • Learn key AI technologies, hardware and development frameworks.
  • Explore AI as a Service (AIaaS), including contracts and examples.
  • Understand how pre-trained models accelerate ML development.
  • Learn how transfer learning reuses existing model knowledge.
  • Recognize risks like bias, drift, and instability when using pre-trained models.
  • Explore supervised, unsupervised and reinforcement learning.
  • Understand the complete ML workflow from data to deployment.
  • Learn how to select ML techniques based on problem and data.
  • Identify overfitting and underfitting with hands-on demonstrations.
  • Get an overview of global AI standards, regulations and ethical practices.

DAY 2:
AI Quality Characteristics, Risks & Data Foundations

  • Explore flexibility, adaptability, autonomy and evolution.
  • Understand challenges like bias, ethics, side effects and reward hacking.
  • Learn transparency, interpretability, explainability and safety aspects.
  • Understand the role of data preparation in the ML workflow.
  • Identify challenges in cleaning, preprocessing and validating ML data.
  • Learn the purpose of training, validation and test datasets.
  • Perform hands-on activities on dataset creation and ML model building.
  • Explore dataset issues like imbalance, noise, drift and leakage.
  • Understand how data quality impacts model accuracy and behavior.
  • Learn supervised learning labeling methods and mislabeled data risks.

DAY 3:
Metrics, Neural Networks & Testing AI-Based Systems

  • Learn confusion matrix fundamentals for evaluating classification models.
  • Understand metrics for classification, regression and clustering.
  • Identify limitations in commonly used ML metrics.
  • Practice evaluating ML models with benchmark suites.
  • Understand how neural networks work through perceptron implementation.
  • Learn coverage techniques for neural network testing.
  • Understand how to specify and document AI components.
  • Explore testing levels: input data, model, component, integration, system and acceptance.
  • Learn how to design test data and detect automation bias.
  • Understand how to test for concept drift and choose the right test approach.
  • Learn methods to test for bias, transparency and explainability.
  • Understand testing challenges in self-learning, autonomous, probabilistic and complex AI systems.
  • Learn about test oracles, acceptance criteria and model explainability.

DAY 4:
Advanced AI Testing Techniques & AI for Testing

  • Learn adversarial attacks, data poisoning and their impact.
  • Understand pairwise testing, back-to-back testing and A/B testing.
  • Apply metamorphic testing and exploratory data analysis with hands-on sessions.
  • Understand environment needs for AI system validation.
  • Learn how virtual testing environments support AI model evaluation.
  • Explore AI-driven defect analysis and prediction techniques.
  • Learn AI-based test case generation and regression suite optimization.
  • Understand how AI enhances UI testing through intelligent interaction.
  • Review key ISTQB® CT-AI concepts and exam topics.
  • Understand exam structure, format and scoring.
  • Participate in a mock exam followed by a detailed debrief.

COURSE HIGHLIGHTS

What You’ll Get with the ISTQB® CT AI Testing Program

Accelerate your AI-testing journey with a comprehensive, hands-on, globally recognized qualification. Here’s what the program empowers you with:

This certification ensures you can meaningfully evaluate, test, and support AI systems in modern software organizations.

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ASSESSMENT & CERTIFICATION

ISTQB® CT-AI Certification – Exam Snapshot

Number of Questions
0
Total Points
0
Passing Score
0
Exam Duration
0 min

Extra Time

+25% for non-native language candidates

Eligibility

CTFL certification required to obtain CT-AI certification

SYLLABUS DOWNLOAD

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ISTQB® CT AI Testing

Get a detailed overview of the course content and structure. Download the syllabus to see what you'll be learning and how our program can help you achieve your career goals.

PREREQUISITES

Who Should Enroll for the CT AI Testing Program

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Testing & QA Professionals

Testers, test analysts, test engineers, automation engineers, QA leads, test managers.

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Technology & Delivery Teams

Software developers, project managers, product managers, team leaders, IT directors.

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AI & Data Professionals

Data analysts, data engineers, ML practitioners involved in model quality validation.

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Anyone Working with AI-Enabled Applications

Ideal for those needing practical understanding of how to verify, validate, and trust AI systems.

MEET THE TEAM

Get to know our experts that's invested in your success.

TESTIMONIALS

Hear From Our Participants

COURSE FEES

Professional tuition fees & corporate training options

Individual

Individual Training

Personalized learning tailored to your pace, schedule, and career goals.

₹24,999

Enterprise

Corporate Training

Scalable training solutions for teams of any size, across all domains.

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Frequently Asked Questions (FAQs)

1. What are the pre-requisites for enrolling in the CT AI Testing?

No prior AI experience is required. However, an ISTQB® CTFL certification is mandatory to take the CT-AI exam.

The instructor-led program typically runs for 2–3 days, depending on the delivery plan. Self-learn participants can progress at their own pace using structured learning materials.

The course is available in instructor-led online and self-learn online formats. Instructor-led sessions include live interaction and guided learning, while self-learn offers flexibility with recorded content and references.

The program balances conceptual learning with practical exposure to AI and ML systems, including AI-supported testing techniques and model evaluation. The syllabus is tool-neutral and focuses on principles applicable across AI platforms and frameworks.

CT-AI certification builds expertise in one of the fastest-growing areas of software quality, enabling you to contribute to AI risk management and positioning you for advanced QA and AI testing specialist roles.

ENROLL NOW

Become a ISTQB® CT-AI Certified Analyst

Reserve your spot in advance and choose the schedule that best fits your availability.

Still have a doubt, Enquire now!

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