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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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 6

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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
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?
SELECT ONE OPTION

A. ML model metrics to evaluate the functional performance
B. Different features like ADAS, Lane Change Assistance etc.
C. Different weather conditions
D. Different Road Types

Answer: A

Explanation:
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
* Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
* Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
* ML Model Metrics to Evaluate Functional Performance (C): 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.
* Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self- driving cars.
* Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

NEW QUESTION # 50
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

A. Nature of data constantly changes with lime.
B. Test data being sourced from public sources.
C. Compliance needs require proper care to be taken of input personal data.
D. Data for the use case is being generated at a fast pace.

Answer: D

Explanation:
* Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.

NEW QUESTION # 51
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options.
When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "Idon't understand your question." This AI system should be categorized as?

A. Narrow AI
B. General AI
C. Super AI
D. Conventional AI

Answer: A

Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general intelligence or consciousness. The AI system in the question is capable of recognizing speech and responding to specific booking-related requests but fails when asked about unrelated topics (such as weather or labor markets).
* Option A:"General AI"
* Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can. The described system is task-specific and does not exhibit general intelligence.
* Option B:"Narrow AI"
* Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks broader cognitive abilities.
* Option C:"Super AI"
* Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity.
The AI in the scenario is far from this level.
* Option D:"Conventional AI"
* Incorrect. Conventional AI is a broader term that may include rule-based systems. The described system relies on machine learning and natural language processing, making it more aligned with Narrow AI.
* Definition of Narrow AI:"Narrow AI refers to AI systems that are designed to perform a single task or a limited set of tasks, without general intelligence".
* General vs. Narrow AI:"General AI remains an area of research, while most current AI applications fall into the category of Narrow AI".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option B is the correct categorization for the AI-based ticket booking system.

NEW QUESTION # 52
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

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

Answer: C

Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
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.
Clarifying the Other Options:
A . A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B . A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D . A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
Reference:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).

NEW QUESTION # 53
Which of the following is an example of overfitting?

A. The model is not able to generalize to accommodate new types of data.
B. The model is missing relationships between the inputs and outputs.
C. The model is too simplistic for the data.
D. The model discards data it considers to be noise or outliers.

Answer: A

Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

NEW QUESTION # 54
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