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| Section | Weight | Objectives |
|---|---|---|
| Input Data Testing for Machine Learning Systems | 20% | - Testing for bias, representativeness, and completeness - Label verification and ground truth assessment - Validation of data pipelines and preprocessing - Data quality attributes |
| Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs |
| Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Characteristics of AI and ML systems - Challenges in testing AI-based systems |
| Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Model performance and accuracy testing - Adversarial and robustness testing - Explainability and transparency testing - Metamorphic and statistical testing |
| Test Levels and Machine Learning Systems | 15% | - Testing across ML lifecycle stages - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects |
| Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - A/B testing and back-to-back testing - Risk-based testing - Test environment and tool considerations |
| Machine Learning Development Testing | 10% | - Testing MLOps and deployment pipelines - Regression testing for retrained models - Testing ML development workflows |
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NEW QUESTION # 146
"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
Answer: B
Explanation:
Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
NEW QUESTION # 147
Which statement about using AI to analyze reported defects is MOST correct?
Answer: C
Explanation:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models.
Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, Option C is correct: AI canidentify defect categories, supporting assignment and triage.
NEW QUESTION # 148
Which statement regarding data preparation in the ML workflow is correct?
Answer: C
Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation. Data preparation consists ofdata gathering,cleaning, transformation, andsampling.
The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C.
NEW QUESTION # 149
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?
Answer: C
Explanation:
The correct answer is A . For autonomous systems, the distinguishing test-environment issue is that the system must respond to changes in its environment without human intervention and must also recognize situations where autonomy should be ceded back to human operators. The CT-AI syllabus states that, for some autonomous systems, identifying and mimicking the circumstances for ceding autonomy may require test environments to push the systems to extremes . It also notes that some autonomous systems operate in hazardous environments, making representative real-world testing difficult or unsafe.
Option B relates more specifically to multi-agent AI systems, where the environment may need non- determinism to mimic other interacting AI-based systems. Option C is a general AI-environment consideration where AI-specific processors may need inclusion. Option D is associated with explainability, where tools may be needed to understand decisions. These are valid AI test-environment factors, but they are not the best autonomy-specific reason. Virtual environments are particularly valuable because dangerous, unusual, and extreme scenarios can be tested safely and repeatedly.
References/topics: CT-AI Syllabus Chapter 10, Sections 10.1 and 10.2 "Test Environments for AI-Based Systems."
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NEW QUESTION # 150
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?
Answer: C
Explanation:
The correct answer is B . In unsupervised learning, the ML model is created from unlabeled data . The algorithm infers patterns in the input data and assigns inputs to groups or classes based on commonalities, rather than learning from known output labels. The CT-AI syllabus identifies clustering and association as the main unsupervised learning problem types.
Missing information about whether previous loans were granted and repaid is essentially missing outcome label information. That would be highly relevant for supervised learning, where the model learns from input data and corresponding labels, but it is least directly relevant to an unsupervised approach. By contrast, options A, C, and D are classic dataset quality issues. Missing account records indicate incomplete data; inconsistent pre-processing can create data-format and comparability defects; and irrelevant account information may adversely influence results or waste modelling resources. The syllabus explicitly lists incomplete data, data not pre-processed, and irrelevant data as typical dataset quality issues affecting ML models.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.2 "Unsupervised Learning"; Chapter 4, Section 4.3 "Dataset Quality Issues"; Chapter 4, Section 4.4 "Data Quality and its Effect on the ML Model."
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NEW QUESTION # 151
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