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| Section | Weight | Objectives |
|---|---|---|
| Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - AI-specific quality characteristics - Challenges in testing AI-based systems |
| Machine Learning Development Testing | 10% | - Testing MLOps and deployment pipelines - Testing ML development workflows - Regression testing for retrained models |
| Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - Risk-based testing - Test environment and tool considerations - A/B testing and back-to-back testing |
| Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Metamorphic and statistical testing - Model performance and accuracy testing - Explainability and transparency testing - Adversarial and robustness testing |
| Test Levels and Machine Learning Systems | 15% | - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects - Testing across ML lifecycle stages |
| Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Generative AI fundamentals - Specific risks and test approaches for LLMs |
| Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Testing for bias, representativeness, and completeness - Validation of data pipelines and preprocessing - Label verification and ground truth assessment |
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NEW QUESTION # 128
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: C
Explanation:
Dynamic testing for inappropriate bias often requires obtaining additional attributes about the data being processed. This is because bias can emerge based on hidden or unexamined features that influence the system's behavior. By collecting and analyzing more attributes, testers can better understand potential biases in the data and how they affect the AI system's decisions.
NEW QUESTION # 129
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age.
When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age.
Which ONE of the following approaches do you expect to be MOST likely to succeed?
Answer: D
Explanation:
A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age verification.
NEW QUESTION # 130
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as
"AI" changes over time. Technologies once considered AI-such as expert systems from the 1970s and 1980s or early chess-playing systems-are no longer viewed as AI today. This phenomenon is explicitly labeled the
"AI Effect,"described as"the changing perception of what constitutes AI."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .
Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect.
Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.
NEW QUESTION # 131
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: A
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 # 132
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?
Answer: C
Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
NEW QUESTION # 133
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