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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 3	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 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 8	systems from those required for conventional systems.
Topic 9	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 10	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 11	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

A. Inherited bias: the model could have inherited unknown defects
B. There is no risk, as the model has already been trained
C. Insufficient function; the model was not trained to check for colors or words
D. Improper data preparation

Answer: A

Explanation:
A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:
* Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.
* Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.
* Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.
This inherited bias can result in:
* False Positives:Recognizing other brand logos as the beer company's logo.
* False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).
* Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.
Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.
* Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 11
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Grouping similar products together before feeding them into the algorithm
B. Selecting the correct data pipeline for the ML training
C. Labeling the data correctly
D. Minimizing the amount of time spent training the algorithm

Answer: C

Explanation:
Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.
* Supervised Learning Process
* The algorithm learns from labeled data, mapping inputs to correct outputs during training.
* If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.
* Quality of Training Data
* The accuracy of any supervised ML model ishighly dependent on the quality of labels.
* Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.
* Error Minimization and Model Accuracy
* Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.
* It leads to overfitting or underfitting, which decreases the model's ability to generalize.
* Industry Standard Practices
* Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.
* (B) Minimizing the amount of time spent training the algorithm#(Incorrect)
* While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.
* (C) Selecting the correct data pipeline for the ML training#(Incorrect)
* A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.
* (D) Grouping similar products together before feeding them into the algorithm#(Incorrect)
* This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.
* Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."
* Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly." Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.

NEW QUESTION # 12
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A. A system that is fully able to respond to its environment.
B. A system that utilizes a tool like Selenium.
C. A fully automated manufacturing plant that uses no software.
D. A system that utilizes human beings for all important decisions.

Answer: A

Explanation:
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

NEW QUESTION # 13
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. Tuning the model
B. Deploying the model
C. Evaluating the model
D. Data testing

Answer: A

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 # 14
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

A. Biased data
B. The data pipeline
C. The number of classes
D. The quality of the labeling

Answer: C

Explanation:
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.

NEW QUESTION # 15
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