ISTQB CT-AI Certification Exam Syllabus

CT-AI dumps PDF, ISTQB CT-AI Braindumps, free Artificial Intelligence Tester dumps, AI Testing dumps free downloadTo achieve the professional designation of ISTQB Certified Tester AI Testing from the ISTQB, candidates must clear the CT-AI Exam with the minimum cut-off score. For those who wish to pass the ISTQB AI Testing certification exam with good percentage, please take a look at the following reference document detailing what should be included in ISTQB Artificial Intelligence Tester Exam preparation.

The ISTQB CT-AI Exam Summary, Body of Knowledge (BOK), Sample Question Bank and Practice Exam provide the basis for the real ISTQB Certified Tester AI Testing (CT-AI) exam. We have designed these resources to help you get ready to take ISTQB Certified Tester AI Testing (CT-AI) exam. If you have made the decision to become a certified professional, we suggest you take authorized training and prepare with our online premium ISTQB AI Testing Practice Exam to achieve the best result.

ISTQB CT-AI Exam Summary:

Exam Name ISTQB Certified Tester AI Testing
Exam Code CT-AI
Exam Fee USD $199
Exam Duration 60 Minutes
Number of Questions 40
Passing Score 31/47
Format Multiple Choice Questions
Schedule Exam Pearson VUE
Sample Questions ISTQB Artificial Intelligence Tester Exam Sample Questions and Answers
Practice Exam ISTQB Certified Tester AI Testing (CT-AI) Practice Test

ISTQB AI Testing Syllabus Topics:

Topic Details

Introduction to AI - 105 minutes

Definition of AI and AI Effect - Describe the AI effect and how it influences the definition of AI.
Narrow, General and Super AI - Distinguish between narrow AI, general AI, and super AI.
AI-Based and Conventional Systems. - Differentiate between AI-based systems and conventional systems.
AI Technologies - Recognize the different technologies used to implement AI.
AI Development Frameworks - Identify popular AI development frameworks.
Hardware for AI-Based Systems - Compare the choices available for hardware to implement AI-based systems.
AI as a Service (AIaaS) - Explain the concept of AI as a Service (AIaaS).
Pre-Trained Models - Explain the use of pre-trained AI models and the risks associated with them.
Standards, Regulations and AI - Describe how standards apply to AI-based systems.

Quality Characteristics for AI-Based Systems - 105 minutes

Flexibility and Adaptability - Explain the importance of flexibility and adaptability as characteristics of AI-based systems.
Autonomy - Explain the relationship between autonomy and AI-based systems.
Evolution - Explain the importance of managing evolution for AI-based systems.
Bias - Describe the different causes and types of bias found in AI-based systems.
Ethics - Discuss the ethical principles that should be respected in the development, deployment and use of AI-based systems.
Side Effects and Reward Hacking - Explain the occurrence of side effects and reward hacking in AI-based systems.
Transparency, Interpretability and Explainability - Explain how transparency, interpretability and explainability apply to AI-based systems.
Safety and AI - Recall the characteristics that make it difficult to use AI-based systems in safetyrelated applications.

Machine Learning (ML) - Overview - 145 minutes

Forms of ML - Describe classification and regression as part of supervised learning.
- Describe clustering and association as part of unsupervised learning.
- Describe reinforcement learning.
ML Workflow - Summarize the workflow used to create an ML system.
Selecting a Form of ML - Given a project scenario, identify an appropriate form of ML (from classification, regression, clustering, association, or reinforcement learning).
Factors involved in ML Algorithm Selection - Explain the factors involved in the selection of ML algorithms.
Overfitting and Underfitting - Summarize the concepts of underfitting and overfitting.
- Demonstrate underfitting and overfitting.

ML - Data - 230 minutes

Data Preparation as part of the ML Workflow - Describe the activities and challenges related to data preparation.
- Perform data preparation in support of the creation of an ML model.
Training, Validation and Test Datasets in the ML Workflow - Contrast the use of training, validation and test datasets in the development of an ML model.
- Identify training and test datasets and create an ML model.
Dataset Quality Issues - Describe typical dataset quality issues.
Data quality and its effect on the ML model - Recognize how poor data quality can cause problems with the resultant ML model.
Data Labelling for Supervised Learning - Recall the different approaches to the labelling of data in datasets for supervised learning.
- Recall reasons for the data in datasets being mislabeled.

ML Functional Performance Metrics - 120 minutes

Confusion Matrix - Calculate the ML functional performance metrics from a given set of confusion matrix data.
Additional ML Functional Performance Metrics for Classification, Regression and Clustering - Contrast and compare the concepts behind the ML functional performance metrics for classification, regression and clustering methods.
Limitations of ML Functional Performance Metrics - Summarize the limitations of using ML functional performance metrics to determine the quality of the ML system.
Selecting ML Functional Performance Metrics - Select appropriate ML functional performance metrics and/or their values for a given ML model and scenario.
- Evaluate the created ML model using selected ML functional performance metrics
Benchmark Suites for ML - Explain the use of benchmark suites in the context of ML

ML - Neural Networks and Testing - 65 minutes

Neural Networks - Explain the structure and function of a neural network including a DNN.
- Experience the implementation of a perceptron.
Coverage Measures for Neural Networks - Describe the different coverage measures for neural networks.

Testing AI-Based Systems Overview - 115 minutes

Specification of AI-Based Systems - Explain how system specifications for AI-based systems can create challenges in testing.
Test Levels for AI-Based Systems - Describe how AI-based systems are tested at each test level
Test Data for Testing AI-Based Systems - Recall those factors associated with test data that can make testing AI-based systems difficult.
Testing for Automation Bias in AI-Based Systems - Explain automation bias and how this affects testing.
Documenting an ML Model - Describe the documentation of an AI component and understand how documentation supports the testing of AI-based systems.
Testing for Concept Drift - Explain the need for frequently testing the trained model to handle concept drift.
Selecting a Test Approach for an ML System - For a given scenario determine a test approach to be followed when developing an ML system.

Testing AI-Specific Quality Characteristics - 150 minutes

Challenges Testing Self-Learning Systems - Explain the challenges in testing created by the self-learning of AI-based systems.
Testing Autonomous AI-Based Systems - Describe how autonomous AI-based systems are tested
Testing for Algorithmic, Sample and Inappropriate Bias - Explain how to test for bias in an AI-based system.
Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems - Explain the challenges in testing created by the probabilistic and non-deterministic nature of AI-based systems.
Challenges Testing Complex AI-based Systems - Explain the challenges in testing created by the complexity of AI-based systems.
Testing the Transparency, Interpretability and Explainability of AI-based Systems - Describe how the transparency, interpretability and explainability of AI-based systems can be tested.
- Use a tool to show how explainability can be used by testers.
Test Oracles for AI-Based Systems - Explain the challenges in creating test oracles resulting from the specific characteristics of AI-based systems.
Test Objectives and Acceptance Criteria - Select appropriate test objectives and acceptance criteria for the AI-specific quality characteristics of a given AI-based system.

Methods and Techniques for the Testing of AI-Based Systems - 245 minutes

Adversarial Attacks and Data Poisoning - Explain how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Pairwise Testing - Explain how pairwise testing is used for AI-based systems.
- Apply pairwise testing to derive and execute test cases for an AI-based system.
Back-to-Back Testing - Explain how back-to-back testing is used for AI-based systems.
A/B Testing - Explain how A/B testing is applied to the testing of AI-based systems.
Metamorphic Testing - Apply metamorphic testing for the testing of AI-based systems.
- Apply metamorphic testing to derive test cases for a given scenario and execute them.
Experience-Based Testing of AI-Based Systems - Explain how experience-based testing can be applied to the testing of AI-based systems.
- Apply exploratory testing to an AI-based system.
Selecting Test Techniques for AI-Based Systems - For a given scenario, select appropriate test techniques when testing an AI-based system.

Test Environments for AI-Based Systems - 30 minutes

Test Environments for AI-Based Systems - Describe the main factors that differentiate the test environments for AI-based systems from those required for conventional systems.
Virtual Test Environments for Testing AI-Based Systems - Describe the benefits provided by virtual test environments in the testing of AI-based systems.

Using AI for Testing - 195 minutes

AI Technologies for Testing - Categorize the AI technologies used in software testing.
- Discuss, using examples, those activities in testing where AI is less likely to be used.
Using AI to Analyze Reported Defects - Explain how AI can assist in supporting the analysis of new defects.
Using AI for Test Case Generation - Explain how AI can assist in test case generation.
Using AI for the Optimization of Regression Test Suites - Explain how AI can assist in optimization of regression test suites
Using AI for Defect Prediction - Explain how AI can assist in defect prediction.
- Implement a simple AI-based defect prediction system.
Using AI for Testing User Interfaces - Explain the use of AI in testing user interfaces

Both ISTQB and veterans who’ve earned multiple certifications maintain that the best preparation for a ISTQB CT-AI professional certification exam is practical experience, hands-on training and practice exam. This is the most effective way to gain in-depth understanding of ISTQB Artificial Intelligence Tester concepts. When you understand techniques, it helps you retain ISTQB AI Testing knowledge and recall that when needed.

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