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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Testing Deep Learning Models |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Explainability and Interpretability - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score |
| Topic 3: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing |
| Topic 4: Testing AI-Based Systems | 20% | - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems |
| Topic 5: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Definitions and Terminology |
| Topic 6: Practical Considerations | 12% | - Test Environment Setup for AI - Test Data Quality and Preparation - Automation in AI Testing - Documentation and Reporting |
In today's world, the Certified Tester AI Testing Exam (CT-AI) certification exam has become increasingly popular, providing professionals with the opportunity to upskill and stay competitive in the tech industry. At ExamTorrent, we understand the importance of obtaining the ISTQB CT-AI Certification in the ISTQB sector, where technological advancements constantly evolving.
NEW QUESTION # 116
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: B
Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful.
Therefore, the operational model should be regularly evaluated against its acceptance criteria."
NEW QUESTION # 117
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
Answer: B
Explanation:
This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.
NEW QUESTION # 118
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
Answer: D
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 119
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
Answer: C
Explanation:
This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
NEW QUESTION # 120
You are using a neural network to train a robot vacuum to navigate without bumping into objects.
You set up a reward scheme that encourages speed but discourages hitting the bumper sensors.
Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back. This is an example of what type of behavior?
Answer: B
Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a
'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function--driving backwards to avoid bumper triggers while maximizing reward for speed.
NEW QUESTION # 121
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