ISQI CT-AI_v1.0_World Exam Collection - ISTQB Certified Tester AI Testing (v1.0)

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

Question #1

An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

  • A. This is an example of sample bias.
  • B. This is an example of hyperparameter bias.
  • C. This is an example of expert system bias.
  • D. This is an example of algorithmic bias.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

  • A. Data testing
  • B. Tuning the model
  • C. Evaluating the model
  • D. Deploying the model
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

  • A. I and II
  • B. Only III
  • C. I and III
  • D. Only II
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to bebehaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION

  • A. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
  • B. There was an algorithmic bias in the Al system.
  • C. The difficulty of defining criteria for improvement before the model can be accepted.
  • D. The fast pace of change did not allow sufficient time for testing.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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