AI Testing (CT-AI) Practice Test
Gana confianza para AI Testing (CT-AI). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
The AI Testing CT-AI exam is a comprehensive 45-question assessment designed to validate a professional's practical knowledge and skills in testing and validating artificial intelligence systems. This exam evaluates a candidate's ability to apply software testing fundamentals to the unique challenges of AI, including testing non-deterministic systems, validating machine learning models, assessing data quality, and ensuring ethical AI behavior. It covers the full AI testing lifecycle from data preparation and model validation to integration and monitoring in production. This exam is ideal for software testers, quality assurance engineers, data scientists, and AI developers who are involved in building, deploying, or maintaining AI-powered applications. Professionals in roles responsible for AI governance, risk, and compliance will also find it highly relevant. By earning this certification, candidates demonstrate a structured, critical approach to AI quality, distinguishing themselves as capable of mitigating the unique risks associated with intelligent systems. Success signifies an understanding of how to build trust in AI outputs and ensure these systems perform reliably, fairly, and as intended in real-world scenarios.
Preguntas de Muestra
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Autonomous Delivery Robot Navigation An autonomous robot uses a vision-based AI system to navigate city sidewalks. It must identify obstacles (static and moving), follow pedestrian right-of-way norms, and adhere to predefined routes.
Arrange the following testing phases for this AI system in the order they should typically be conducted, from earliest to latest. 1. On-road, real-world integration testing with other vehicles and pedestrians. 2. Unit testing of individual perception and path-planning algorithms in isolation. 3. Closed-course testing with physical prototypes under controlled, simulated urban conditions. 4. Simulation-based testing in a high-fidelity virtual environment with digital twins of the city.
In the context of testing a computer vision model for autonomous vehicle perception, which of the following is considered a 'corner case' or 'edge case' that dedicated test suites should target?
MediScan AI MediScan AI is a diagnostic support tool that analyzes medical images (X-rays) to identify potential fractures. The development team is preparing for a final validation test before deployment. They have a dataset of 10,000 annotated X-ray images, split 70/30 for training and testing.
Which of the following testing strategies are MOST critical to include in their validation plan to assess the model's real-world reliability and fairness? (Select all that apply).
Image Classifier for Medical Diagnosis A team is developing a convolutional neural network (CNN) to classify skin lesion images as 'benign' or 'malignant'. The training dataset contains 9,000 benign images and 1,000 malignant images. After training, the model achieves 95% accuracy on a held-out test set with the same class distribution.
Given this scenario, which TWO actions are MOST critical for a tester to recommend before deploying this model? (Select TWO).
When performing concept drift detection in a production machine learning model for dynamic pricing, which combination of monitoring signals provides the MOST comprehensive early warning?
Qué temas cubre este examen
Usa las ponderaciones publicadas de los dominios para planificar tu estudio. Los resultados de práctica no predicen tu puntuación en el examen de certificación.