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

CT-AI_v1.0_World actual test
  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)
  • Updated: Sep 02, 2026
  • Q & A: 40 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About ISQI CT-AI_v1.0_World Exam

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

Question 1

"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A. Maximize accuracy and recall
B. Maximize precision and accuracy
C. Maximize specificity number of classes
D. Maximize recall and precision


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. Evaluating the model
C. Tuning the model
D. Deploying the model


Question 3

In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for accuracy
B. Testing for concept drift
C. Testing for security
D. Testing for bias


Question 4

"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION

A. A lack of focus on non-functional requirements testing.
B. A lack of similarity between the training and testing data.
C. The input data has not been tested for quality prior to use for testing.
D. A lack of focus on choosing the right functional-performance metrics.


Solutions:

Question 1
Answer: D
Question 2
Answer: C
Question 3
Answer: B
Question 4
Answer: B

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