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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing AI-Specific Quality Characteristics | 20% | - Explainability and Interpretability - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Robustness Testing |
| Topic 2: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Metamorphic Testing - Black-Box Testing for AI Systems - Adversarial Testing |
| Topic 3: Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations |
| Topic 4: Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting |
| Topic 5: Basics of AI | 8% | - AI Types and Techniques - Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) |
| Topic 6: Practical Considerations | 12% | - Test Data Quality and Preparation - Automation in AI Testing - Documentation and Reporting - Test Environment Setup for AI |
>> CT-AI Latest Dumps Files <<
The web-based ISTQB CT-AI mock test is compatible with mamy systems. This version of the ISTQB CT-AI practice exam requires an active internet connection. It does not require any additional plugins or software installation to operate. Furthermore, others also support the CT-AI web-based practice exam. Features of the CT-AI desktop practice exam software are web-based as well.
NEW QUESTION # 102
Which ONE of the below types of testing is NOT a type of experience based testing applied to an AI based system?
Answer: D
Explanation:
The correct answer is B . The CT-AI syllabus identifies experience-based testing as including error guessing, exploratory testing, and checklist-based testing , all of which may be applied to AI-based systems. It also explicitly relates exploratory testing to exploratory data analysis, where data is examined for patterns, relationships, trends, outliers, distribution, format, and ranges.
Option A matches exploratory testing and EDA, because the tester explores training data to guide subsequent testing. Option C matches error guessing, because the tester uses knowledge of previous ML-system failures, such as biased training data, to identify likely faults. Option D matches checklist-based testing, because the syllabus cites Google's ML test checklist as an effective checklist approach for ML systems.
Option B is not, by itself, an experience-based AI testing type. Usability testing may be important for AI- based systems, especially where users consume predictions, recommendations, or explanations, but it is classified as a quality-characteristic or user-experience concern, not as one of the named experience-based techniques in this syllabus context.
References/topics: CT-AI Syllabus Chapter 9, Section 9.6 "Experience-Based Testing of AI-Based Systems"; Section 9.6.1 "Exploratory Testing and EDA."
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NEW QUESTION # 103
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: A
NEW QUESTION # 104
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: B
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 # 105
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
Answer: D
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
References:
* ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
* Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
NEW QUESTION # 106
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
Answer: C
Explanation:
While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
NEW QUESTION # 107
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