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  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning- ML Fundamentals
  • 1. Machine Learning Overview
  • 2. Supervised and Unsupervised Learning
  • 3. Training and Validation
Topic 2: Test Environments for AI-Based Systems- AI Test Infrastructure
  • 1. Data Management
  • 2. Environment Configuration
  • 3. Monitoring and Maintenance
Topic 3: Introduction to AI- AI Fundamentals
  • 1. AI Definitions and Concepts
  • 2. AI Technologies
  • 3. AI Development Frameworks
  • 4. AI-Based vs Conventional Systems
  • 5. AI as a Service
Topic 4: ML Functional Performance Metrics- Performance Evaluation
  • 1. Accuracy
  • 2. Precision
  • 3. Recall
  • 4. F1 Score
Topic 5: Testing AI-Based Systems Overview- AI Testing Fundamentals
  • 1. Test Strategy
  • 2. AI Test Lifecycle
  • 3. Risk-Based Testing
Topic 6: Testing AI-Specific Quality Characteristics- Quality Validation
  • 1. Fairness Testing
  • 2. Explainability Assessment
  • 3. Robustness Testing
Topic 7: ML Neural Networks and Testing- Neural Networks
  • 1. Testing Neural Networks
  • 2. Neural Network Basics
Topic 8: Quality Characteristics for AI-Based Systems- AI-Specific Quality Attributes
  • 1. Bias
  • 2. Ethics
  • 3. Evolution
  • 4. Transparency and Explainability
  • 5. Autonomy
  • 6. Flexibility and Adaptability
Topic 9: Methods and Techniques for Testing AI-Based Systems- Test Design Techniques
  • 1. Scenario-Based Testing
  • 2. Model Testing
  • 3. Data-Oriented Testing
Topic 10: Using AI for Testing- AI-Assisted Testing
  • 1. Test Automation Support
  • 2. Defect Prediction
  • 3. Test Data Generation
Topic 11: ML Data- Data Quality
  • 1. Data Collection
  • 2. Data Bias
  • 3. Data Preparation

ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:

Question #1

A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

  • A. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
  • B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
  • C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
  • D. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Answer: C

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Question #2

Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

  • A. Threshold coverage
  • B. Value coverage
  • C. Neuron coverage
  • D. Sign change coverage
Answer: D

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Question #3

Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

  • A. Search engines
  • B. Procedural programming
  • C. Case control structures
  • D. Genetic algorithms
Answer: D

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Question #4

Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

  • A. Al systems are inherently flexible.
  • B. Flexible Al systems allow for easier modification of the system as a whole.
  • C. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
  • D. Al systems require changing of operational environments; therefore, flexibility is required.
Answer: B

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