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[General] ISTQB CT-AI Exam | Valid CT-AI Test Pass4sure - 10 Years of Excellence of CT-AI

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【General】 ISTQB CT-AI Exam | Valid CT-AI Test Pass4sure - 10 Years of Excellence of CT-AI

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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
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
  • 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
  • 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
  • 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 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
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q32-Q37):NEW QUESTION # 32
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails 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. 90.0%
  • C. 98.9%
  • D. 94.5%
Answer: A
Explanation:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:
Precision=TPTP+FP×100%        ext{Precision} = rac{TP}{TP + FP}         imes 100%Precision=TP+FPTP×100% Where:
* TP (True Positives)= Number of correctly predicted positive cases
* FP (False Positives)= Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision.
* Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation of precisionusing the confusion matrix.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 33
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. Different features like ADAS, Lane Change Assistance etc.
  • B. ML model metrics to evaluate the functional performance
  • C. Different Road Types
  • D. Different weather conditions
Answer: B
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 # 34
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
  • A. Selecting testing data from a different dataset than the training dataset
  • B. Selecting training data by purposely excluding specific faulty conditions
  • C. Selecting testing data from a boat manufacturer's bolt longevity data
  • D. Selecting training data by purposely including all known faulty conditions
Answer: B
Explanation:
Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.
This results in:
* Sample bias, where the training data is not fully representative of all possible faulty conditions.
* Algorithmic bias, where the model prioritizes certain defect types while ignoring others.
* B. Selecting training data by purposely including all known faulty conditions# This would help reduce bias by improving model generalization.
* C. Selecting testing data from a different dataset than the training dataset# This is a good practice to evaluate model generalization but does not inherently introduce bias.
* D. Selecting testing data from a boat manufacturer's bolt longevity data# While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.
* Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions.
Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 35
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. Mobile operating systems cannot process machine learning algorithms
  • 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: A
Explanation:
The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:
"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3, page 22 of 99)

NEW QUESTION # 36
Which of the following is a dataset issue that can be resolved using pre-processing?
  • A. Invalid data
  • B. Numbers stored as strings
  • C. Insufficient data
  • D. Wanted outliers
Answer: B
Explanation:
Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.
Explanation of Answer Choices:
* Option A: Insufficient data
* Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed.
* Option B: Invalid data
* Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity.
* Option C: Wanted outliers
* Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing.
* Option D: Numbers stored as strings
* Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format.
ISTQB CT-AI Syllabus References:
* Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)".

NEW QUESTION # 37
......
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