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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	systems from those required for conventional systems.
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	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 7	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 (Q98-Q103):

NEW QUESTION # 98
You are evaluating the use of a highly accurate pre-trained model that is used widely in industry for a similar use case. There is an intention to apply transfer learning techniques to further customize the model.
Which ONE of the following is the LEAST likely to be a significant risk with this approach?

A. The system may be vulnerable to adversarial attacks inherited from the pre-trained model
B. The functional performance of the system may exhibit bias
C. Differences in data preparation steps between the training of the pre-trained model and consequent use of the model may result in reduced functional performance
D. The functional performance of the pre-trained model could be lower than stakeholders expect

Answer: D

Explanation:
When using a pre-trained model, especially one that is highly accurate and widely used in industry, it is less likely that the functional performance will be lower than expected, given that it has already been proven effective in similar use cases.

NEW QUESTION # 99
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

A. Flexible Al systems allow for easier modification of the system as a whole.
B. Al systems are inherently flexible.
C. Al systems require changing of operational environments; therefore, flexibility is required.
D. Self-learning systems are expected to deal with new situations without explicitly having to program for it.

Answer: A

Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer is C. Flexible AI systems allow for easier modification of the system as a whole.
Reference:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

NEW QUESTION # 100
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. There is no risk, as the model has already been trained
B. Improper data preparation
C. Insufficient function; the model was not trained to check for colors or words
D. Inherited bias: the model could have inherited unknown defects

Answer: D

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 # 101
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Choose ONE option (1 out of 4)

A. Accuracy
B. -
C. Sensitivity
D. F1 value

Answer: D

Explanation:
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non- fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor.
Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.
TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:
* Precision protects legitimate customers by minimizing false alarms.
* Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.

NEW QUESTION # 102
Which of the following aspects is a challenge when handling test data for an AI-based system?

A. Video frame speed or aspect ratio
B. Data frameworks or machine learning frameworks
C. Output data or intermediate data
D. Personal data or confidential data

Answer: D

Explanation:
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI-based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data."

NEW QUESTION # 103
......

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