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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	systems from those required for conventional systems.
Topic 2	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 3	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 4	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 5	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 6	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 7	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 8	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 9	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 10	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 11	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
Which of the following options BEST describes an example of reinforcement learning?

A. A house cleaning robot adjusts the path it takes on a daily basis based on the most efficient route and the amount of dirt it is able to remove.
B. An image recognition system regularly searches the internet to find new examples to add to its training data.
C. A health warning system uses data from thousands of people to predict who is most likely to suffer from a particular health condition based on their behavior.
D. A financial system that chooses which stocks to buy regularly optimizes based on new changes in stock price to improve its performance.

Answer: A

Explanation:
Reinforcement learning involves an agent learning to make decisions by taking actions that maximize a reward over time. In the example of the house cleaning robot, the robot adjusts its actions (path taken) based on feedback (efficiency and amount of dirt removed), which is characteristic of reinforcement learning.

NEW QUESTION # 78
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?

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

Answer: C

Explanation:
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.

NEW QUESTION # 79
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. Mobile operating systems cannot process machine learning algorithms.
B. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
C. The feedback requires a physical connection and cannot be sent over the Internet.
D. The size of the application is consuming too much of the phone's storage capacity.

Answer: B

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 # 80
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

What is the precision of this predictive model?

A. 94.2%
B. 98.9%
C. 94.5%
D. 90.0%

Answer: C

Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)

NEW QUESTION # 81
Which ONE of the following options describes a scenario of A/B testing the LEAST?

A. A comparison of the performance of an ML system on two different input datasets.
B. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
C. A comparison of the performance of two different ML implementations on the same input data.
D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

Answer: A

Explanation:
Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.

NEW QUESTION # 82
......

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