ISTQB Certified AI Tester Complete Training & Exam Prep Test

Explore how artificial intelligence mimics human intelligence to solve problems, recognize patterns, and make decisions, then distinguish narrow, general, and super AI with examples like chess engines and voice assistants.

Explore the shift from traditional software to AI powered systems. See how machine learning learns patterns from thousands of spam and non-spam examples to predict outcomes and adapt.

Explore the ai technologies toolbox, from fuzzy logic and search algorithms to neural networks and clustering, and learn how diverse techniques combine to improve decision making and reliability.

Explore leading ai frameworks like TensorFlow, PyTorch, Keras, MXNet, and scikit-learn, and learn when to use each for data preparation, training, and deployment across cpus, gpus, and cloud.

Explore how CPU, GPU, TPU, and ASICs power AI workloads. Hardware choices influence performance testing, edge AI constraints, and reliable deployment.

Explore ai as a service (aiaas), delivering ready-to-use machine learning models over the internet to simplify data preparation, model training, and deployment. See ai services with pay-as-you-go pricing.

Learn how pretrained models save time by reusing data and how transfer learning customizes them for specific tasks, while managing risks from bias, transparency, data differences, and security.

Explore how ISO, IEC, JTC1 standards and GDPR guide ethical, safe AI governance, risk management, data protection, and transparency across critical industries and applications.

Embrace flexibility, adaptability, and autonomy in AI to handle unforeseen situations, adjust to evolving environments, and operate independently with appropriate human oversight.

Explore how AI systems evolve by learning from environment and experience, improving over time like self-driving cars that adapt to traffic and weather while staying aligned with human values.

Identify and examine biases in AI, including sample, algorithmic, historical, measurement, and exclusion biases, and learn how they affect fairness and accuracy in real-world outcomes.

Explore ethics in AI by examining moral principles, global guidelines like OECD's 2019 principles, and how transparency, human rights, safety, and accountability guide trustworthy, fair, beneficial AI.

Explore AI risks such as side effects and reward hacking, and examine transparency, interpretability, and explainability to build trustworthy, accountable AI in high-stakes domains.

explore AI safety to prevent harm to people, property, and the environment across medicine, manufacturing, defense, security, and transportation, and examine complexity, non-determinism, probabilistic decisions, self-learning, testing, robustness, and transparency.

Explore supervised learning, training models on labeled input-output data to perform classification or regression, with examples like spam filtering and house price forecasting.

Explore unsupervised learning that finds patterns in unlabeled data, grouping items via clustering and uncovering associations to inform customer segmentation, market research, image grouping, and product placement.

Explore reinforcement learning, where an agent learns by interacting with its environment through trial and error, driven by rewards and penalties, as seen in games, autonomous vehicles, and chatbots.

Define objectives and prepare data for a four-part machine learning workflow: planning and data preparation, model generation and tuning, evaluation, and deployment. Iterate training and data cleaning to improve quality.

Train, evaluate, and tune machine learning models to improve accuracy on unseen data. Use training and validation sets, epochs, and hyperparameters to optimize performance and select the best model.

Test the model on unseen data to verify generalization, deploy it to real-world platforms like mobile apps, websites, or cloud servers, and monitor drift with A/B testing and retraining.

Select the right machine learning approach by aligning data type, problem type, and available resources with supervised, unsupervised, or reinforcement methods, considering training data, accuracy, speed, memory, and interpretability.

Master overfitting and underfitting by balancing model complexity to generalize beyond training data, with examples of memorization and simple models and techniques like cross validation and regularization.

Data preparation drives model performance, representing about 43% of ML effort; perform data acquisition, cleaning, structuring, and formatting, label supervised data, and handle diverse formats for accuracy and reliability.

Learn how data cleaning, transformation, augmentation, and sampling prepare raw data for machine learning, including handling missing values, anonymizing sensitive data, and standardizing numerical features to boost model performance.

Master feature engineering by feature selection, feature extraction, and exploratory data analysis (EDA) to boost model accuracy and efficiency before training.

Analyze six main data preparation challenges for machine learning—knowledge requirement, data quality, automating pipelines, cost, errors, and sample bias—tying feature selection and pre-processing to real-world domains.

Split the data into training, validation, and test sets to train, tune, and provide unbiased evaluation on unseen data. Use cross-validation methods like k-fold or leave-one-out when data is limited.

Improve model accuracy by addressing dataset quality issues such as wrong, incomplete, mislabeled, insufficient, and not pre-processed data, while handling bias, privacy, and security concerns.

Improve machine learning quality by ensuring properly labeled data, avoiding mislabels, biases, and security risks, and applying labeling methods like internal, outsourced, crowdsourced, AI-assisted, and hybrid, including Kaggle data.

Explore how a confusion matrix evaluates machine learning predictions and compute accuracy, precision, recall, and F1 score for classification, regression, and clustering.

Explore ROC, AUC, and R squared to evaluate classification and regression models, including true positive rate, false positive rate, MSE, and model fit insights.

Evaluate machine learning models using clustering metrics for unsupervised learning, including intra-cluster, inter-cluster, and silhouette scores, to measure cluster quality and guide metric selection based on the use case.

Explore machine learning benchmark suites that standardize training time benchmarks and inference time benchmarks across algorithms, datasets, and hardware, with examples like ML Commons, Dawnbench, and ML Mark.

Explore neural networks, their layered input to output structure, and how activation values, weights, and bias enable learning through backpropagation and training across epochs.

Explore neural network coverage measures as alternatives to traditional white box criteria, focusing on neuron activation, threshold coverage, and sign change coverage to improve testing and generalization.

Explore advanced neural network coverage techniques: value change coverage, sign sign coverage, and nearest neighbor layer coverage to test neuron sensitivity, inter-layer sign propagation, and redundancy.

Explore neural network testing with tools and frameworks like TensorFlow, Keras, and Deep explore to perform coverage testing and uncover corner cases.

Define specifications for AI based systems while recognizing probabilistic, data-driven behavior. Navigate test levels from input data testing to acceptance testing, covering bias, model testing, and concept drift.

Master the seven testing levels for ai based systems, from input data and model testing to acceptance testing, with best practices for data quality and bias detection.

Explore challenges and best practices in testing ai based systems, including test data, automation bias, documenting an ai component, and concept drift.

Select a test approach for machine learning systems by identifying data quality, pipeline, model, performance, and explainability risks, then apply expert reviews and testing to ensure reliability, usability, and trust.

Master AI testing strategies for self-learning and autonomous AI systems, tackle algorithmic biases and transparency, and apply approaches for probabilistic and non-deterministic models.

Navigate the challenges of testing probabilistic and non-deterministic AI systems, covering variability, transparency, explainability, and test oracles with strategies like metamorphic testing and statistical methods.

Define and implement test objectives and acceptance criteria for AI systems, addressing adaptability, autonomy, transparency, bias, ethics, and safety to mitigate unique AI risks and ensure responsible performance.

Explore adversarial attacks and data poisoning, and pair wise testing, back to back testing, metamorphic testing, and A/B testing for evaluating AI systems.

Explore pairwise testing for AI systems, focusing on parameter pairs to identify defects efficiently. Learn to generate small test sets, illustrated by a smart thermostat, and understand limitations.

Back to back testing, or differential testing, compares the system under test with an independent pseudo oracle to reveal discrepancies and identify defects in AI decisions.

Learn how A/B testing compares two versions of a system using real-world performance metrics, such as click rates and average commute time, to pick a winner.

Explore metamorphic testing as a powerful AI testing technique, using metamorphic relations to verify outputs when inputs change by rotation, brightness, scaling, or cropping.

Explore experience based testing for AI systems, using error guessing, exploratory testing, tours, and exploratory data analysis to uncover biases, edge cases, and robustness issues.

Explore techniques for testing AI based systems, including back-to-back testing, A/B testing, metamorphic testing, adversarial testing, pairwise testing, and experience-based testing, to ensure robustness, fairness, and reliable neural network performance.