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| Section | Weight | Objectives |
|---|---|---|
| Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques |
| Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting - Testing Deep Learning Models |
| Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Bias and Fairness Considerations |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Practical Considerations | 12% | - Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation - Automation in AI Testing |
| Testing AI-Specific Quality Characteristics | 20% | - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection - Robustness Testing |
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NEW QUESTION # 77
Which of the following descriptions of quality aspects of a data set is correct?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2
- Data Preparation, it explains that datasets may suffer from issues such asincomplete data,irrelevant data, incorrect data,unbalanced data, or data lacking preprocessing. "Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.
Option B is incorrect: "data not preprocessed" refers to data that has not undergone normalization, cleaning, or transformation-not data recorded incorrectly. Option C is wrong because irrelevant datadoesnegatively affect ML models by introducing noise and unnecessary features. The syllabus explicitly states that including irrelevant features can degrade model learning. Option D is incorrect: "unbalanced data" relates to disproportionate class distribution, not recency or freshness of data.
Thus, OptionAis the only statement that correctly matches the syllabus definition of this data quality aspect.
NEW QUESTION # 78
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non- fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor.
Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.
TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:
* Precision protects legitimate customers by minimizing false alarms.
* Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.
NEW QUESTION # 79
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
Answer: B
Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
NEW QUESTION # 80
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: D
NEW QUESTION # 81
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?
Answer: B
Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."
NEW QUESTION # 82
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