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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs - Generative AI fundamentals |
| Topic 2: Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Explainability and transparency testing - Model performance and accuracy testing - Adversarial and robustness testing - Metamorphic and statistical testing |
| Topic 3: Test Approaches and Techniques for AI Systems | 10% | - A/B testing and back-to-back testing - Exploratory testing and red teaming - Risk-based testing - Test environment and tool considerations |
| Topic 4: Machine Learning Development Testing | 10% | - Testing ML development workflows - Regression testing for retrained models - Testing MLOps and deployment pipelines |
| Topic 5: Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Label verification and ground truth assessment - Validation of data pipelines and preprocessing - Testing for bias, representativeness, and completeness |
| Topic 6: 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 |
| Topic 7: Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Challenges in testing AI-based systems - Characteristics of AI and ML systems |
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58. Frage
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
Antwort: C
Begründung:
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.
59. Frage
Which ONE of the below is NOT an input to an activation function in a neural network?
Antwort: A
Begründung:
The activation values from the current layer are the output of the activation function and are not used as an input. The input to the activation function consists of the bias, activation values from the previous layer, and the weights assigned to the connections.
60. Frage
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?
Antwort: A
Begründung:
Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.
* Detecting Outliers:
* EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).
* Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.
* Identifying Distribution Shifts:
* By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.
* If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.
* Feature Analysis for Adversarial Patterns:
* EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.
* Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.
* Counteracting Adversarial Attacks:
* Once anomalies are identified, the spam filter's detection rules can be improved by retraining the model on corrected datasets.
* The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.
* Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks."EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data."
* EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts.
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."
* EDA helps validate ML models and detect potential vulnerabilities."The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise." References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.
61. Frage
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI-based systems?
Antwort: B
Begründung:
Combinatorial testing involves testing different combinations of input parameters to ensure coverage of various interactions. In the case of AI-based systems, inputs to the system (such as data features) and environment factors (like conditions under which the system operates) can be considered parameters for pairwise tests. This ensures that different combinations of inputs and environmental factors are tested for their effect on the system's behavior.
62. Frage
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION
Antwort: A
Begründung:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
63. Frage
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