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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing AI-Based Systems	- Test levels for AI systems 1. Model testing 2. System integration testing - Test design techniques 1. Data-driven test design 2. Metamorphic testing
Topic 2: Data Quality and Bias	- Data quality assurance 1. Data completeness and consistency 2. Data labeling quality - Bias and fairness 1. Types of bias in AI systems 2. Fairness testing approaches
Topic 3: Introduction to AI Testing	- AI systems overview 1. Differences between traditional and AI-based systems 2. What is AI and machine learning systems - Challenges in AI testing 1. Data dependency issues 2. Non-determinism in AI systems
Topic 4: Ethics and Risk in AI Testing	- Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles - Risk-based testing for AI 1. Risk identification in AI systems 2. Mitigation strategies
Topic 5: Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Supervised and unsupervised learning 2. Deep learning basics
Topic 6: AI Quality Characteristics	- Quality attributes 1. Explainability and transparency 2. Accuracy and robustness
Topic 7: AI System Lifecycle and Operations	- Deployment and monitoring 1. Model deployment strategies 2. Post-deployment monitoring - Continuous learning systems 1. Model drift detection 2. Retraining strategies

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. Designing a reward function
B. Functional performance
C. The time and associated costs required
D. Specification of acceptance criteria

Answer: C

Explanation:
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.

NEW QUESTION # 153
Which of the following statements about bias in AI-based systems is MOST correct?

A. Inappropriate bias is caused by overweighting of particular classes in algorithms
B. Inappropriate bias is caused by data used for training not being representative of the real world
C. Inappropriate bias only affects ML systems that process data about people
D. Inappropriate bias can be caused by aspects of the algorithm or the data

Answer: B

Explanation:
Inappropriate bias in AI systems is typically caused by the data used for training not being representative of the real world. This can lead to the model making biased or incorrect predictions when applied to real-world scenarios. While algorithmic factors can also contribute to bias, the primary issue arises from biased or unrepresentative training data.

NEW QUESTION # 154
Which of the following statements about explainable AI is correct?
Choose ONE option (1 out of 4)

A. Interpretability refers to how easily users can determine whether the result provided by the AI-based system is correct
B. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
C. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments
D. Explainability refers to how easily the algorithms and training data needed to create the model can be determined

Answer: B

Explanation:
Section2.10 - Explainability and Transparencyof the ISTQB CT-AI syllabus describes explainable AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus referencesThe Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, OptionCdirectly reflects the syllabus content .
Option A is incorrect because interpretability doesnotrefer to determining correctness of outputs; rather, it refers to understandinghowthe model arrives at outputs. Option B incorrectly frames explainability as the ability to investigate algorithms or training data; explainability is aboutunderstanding the model's decision- making, not reverse engineering its components. Option D is incorrect because explainability doesnot eliminate the need for risk and vulnerability assessments; the syllabus clearly emphasizes that testing, risk assessment, and robustness checks remain critical even when a model is explainable.
Thus, the only statement consistent with the syllabus isOption C.

NEW QUESTION # 155
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

A. Test cases to verify that the system automatically suppresses invalid output data
B. Test cases to verify that the system automatically confirms the correct classification of training data
C. Test cases to detect the system prompting for unnecessary human intervention
D. Test cases to detect the system appropriately automating its data input

Answer: C

Explanation:
The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI- based systems:
"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)

NEW QUESTION # 156
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

A. 1,0.9, 0.8
B. 1,0.87,0.84
C. 0.84.1,0.9
D. 0.87.0.9. 0.84

Answer: D

Explanation:
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
* Confusion Matrix:
* Actually Rotten: 45 (True Positive), 8 (False Positive)
* Actually Fresh: 5 (False Negative), 42 (True Negative)
* Accuracy:
* Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
* Formula: Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN
* Calculation: Accuracy=45+4245+42+8+5=87100=0.87\text{Accuracy} = \frac{45 + 42}{45 + 42
+ 8 + 5} = \frac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87
* Recall (Sensitivity):
* Recall is the proportion of true positive results in the total actual positives.
* Formula: Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}Recall=TP+FNTP
* Calculation: Recall=4545+5=4550=0.9\text{Recall} = \frac{45}{45 + 5} = \frac{45}{50} = 0.9 Recall=45+545=5045=0.9
* Specificity:
* Specificity is the proportion of true negative results in the total actual negatives.
* Formula: Specificity=TNTN+FP\text{Specificity} = \frac{TN}{TN + FP}Specificity=TN+FPTN
* Calculation: Specificity=4242+8=4250=0.84\text{Specificity} = \frac{42}{42 + 8} = \frac{42}
{50} = 0.84Specificity=42+842=5042=0.84
Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
References:
ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
"ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

NEW QUESTION # 157
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