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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 2	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 3	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 4	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 5	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 6	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 7	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 8	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 9	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 10	systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

A. Evaluating the model
B. Tuning the model
C. Data testing
D. Deploying the model

Answer: B

Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
* Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
* Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
* Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
* Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC.
Tuning the model.
References:
* ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
* Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.

NEW QUESTION # 60
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Machine learning on logs of execution
B. Analyzing source code for generating test cases
C. GUI analysis by computer vision
D. Natural language processing on textual requirements

Answer: D

Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
* Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
* Why Not Other Options:
* Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
* Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
* GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.

NEW QUESTION # 61
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

A. This is an example of algorithmic bias.
B. This is an example of hyperparameter bias.
C. This is an example of expert system bias.
D. This is an example of sample bias.

Answer: D

Explanation:
A . This is an example of expert system bias.
Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
B . This is an example of sample bias.
Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
C . This is an example of hyperparameter bias.
Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
D . This is an example of algorithmic bias.
Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, option B (sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.

NEW QUESTION # 62
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
B. The feedback requires a physical connection and cannot be sent over the Internet.
C. The size of the application is consuming too much of the phone's storage capacity.
D. Mobile operating systems cannot process machine learning algorithms.

Answer: A

Explanation:

Facial recognition applications involvecomplex computational tasks, including:
* Feature Extraction- Identifying unique facial landmarks.
* Model Training and Updates- Continuous learning and adaptation of user data.
* Image Processing- Handling real-time image recognition under various lighting and angles.
In this scenario, themobile device is experiencing continuous restarts, which suggestsa resource overloadcaused by excessive processing demands.
* Mobile devices have limited computational power.
* Unlike servers, mobile devices lack powerful GPUs/TPUs required for deep learning models.
* On-device learning is computationally expensive.
* The model is likely performingreal-time learning, which can overwhelm the CPU and RAM.
* Continuous feedback transmission may cause overheating.
* If the system is running multiple processes-training, inference, and network communication-it can overload system resources and cause crashes.
* (A) The feedback requires a physical connection and cannot be sent over the Internet.#(Incorrect)
* Feedback transmission over the internet is common for cloud-based AI services.This is not the cause of the issue.
* (B) Mobile operating systems cannot process machine learning algorithms.#(Incorrect)
* Many mobile applications use ML models efficiently. The problem here is thehigh computational intensity, not the OS's ability to run ML algorithms.
* (C) The size of the application is consuming too much of the phone's storage capacity.#(Incorrect)
* Storage issues typically result in installation failures or lag,not device restarts.The issue here isprocessing overload, not storage space.
* AI-based applications require significant computational power."The computational intensity of AI- based applications can pose a challenge when deployed on resource-limited devices."
* Edge devices may struggle with processing complex ML workloads."Deploying AI models on mobile or edge devices requires optimization, as these devices have limited processing capabilities compared to cloud environments." Why is Option D Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option D is the correct answer, as thecomputational demands of the facial recognition system are too high for the mobile hardware to handle, causing continuous restarts.

NEW QUESTION # 63
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

A. The challenge of providing explainability to the decisions made by the system.
B. Challenges in the creation of scenarios of human handover for autonomous systems.
C. The challenge of mimicking undefined scenarios generated due to self-learning
D. Challenges resulting from low accuracy of the models.

Answer: B

Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

NEW QUESTION # 64
......

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CT-AI BRAINDUMPS DOWNLOADS - PREMIUM CT-AI EXAM

CT-AI Braindumps Downloads - Premium CT-AI Exam