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| Section | Weight | Objectives |
|---|---|---|
| Test Levels and Machine Learning Systems | 15% | - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages - Test strategies for AI projects |
| Test Approaches and Techniques for AI Systems | 10% | - Test environment and tool considerations - A/B testing and back-to-back testing - Exploratory testing and red teaming - Risk-based testing |
| Model Testing for Machine Learning Systems | 20% | - Model performance and accuracy testing - Adversarial and robustness testing - Drift detection and monitoring - Explainability and transparency testing - Metamorphic and statistical testing |
| Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs - Generative AI fundamentals |
| Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing ML development workflows - Testing MLOps and deployment pipelines |
| Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Characteristics of AI and ML systems - Challenges in testing AI-based systems |
| Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Testing for bias, representativeness, and completeness - Label verification and ground truth assessment - Validation of data pipelines and preprocessing |
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NEW QUESTION # 141
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
Answer: C
Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* "Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).
NEW QUESTION # 142
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: C
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 # 143
Which assignment of AI techniques to testing support is BEST?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods-one of the three major AI technique groups-are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.
Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.
Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.
NEW QUESTION # 144
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?
Answer: B
Explanation:
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.
NEW QUESTION # 145
Which ONE of the following would be the MOST effective input to an AI based defect prediction tool?
Answer: A
Explanation:
The correct answer is A. Developers associated with previous code changes . The CT-AI syllabus explains that defect prediction may be based on source code metrics, process metrics, and people or organizational metrics. It further states that defect prediction is most effective when based on prior experience in a similar situation, such as the same code base or the same developers. Most importantly, it notes that the best predictors have been found to be people and organizational measures , rather than commonly used source- code metrics such as lines of code and cyclomatic complexity.
Options B, C, and D may still be useful. Cyclomatic complexity and lines of code changed are common code or change metrics, while commit size is a process/change metric. However, the question asks for the most effective input, and the syllabus explicitly prioritizes people and organizational measures. Developer association with prior changes can reveal patterns involving ownership, familiarity, change history, and previous defect introduction, making it the strongest input among the available options.
References/topics: CT-AI Syllabus Chapter 11, Section 11.5 "Using AI for Defect Prediction."
NEW QUESTION # 146
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