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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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 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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 8
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9
  • 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 10
  • 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 11
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
Which ONE of the following options describes a scenario of A/B testing the LEAST?

Answer: B

Explanation:
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.


NEW QUESTION # 96
Which of the following describes the AI effect?

Answer: B

Explanation:
The AI Effectis clearly defined in the ISTQB Certified Tester AI Testing Syllabus v1.0 under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI--such as expert systems from the 1970s and 1980s or early chess-playing systems--are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI ."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .


NEW QUESTION # 97
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.
In the above quote, which ONE of the following options is the correct name of this type of Al?

Answer: D

Explanation:
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.


NEW QUESTION # 98
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

Answer: B

Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretability that interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must consider user background, and explanations must be comprehensible to the intended user group.


NEW QUESTION # 99
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking, status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. One the model is created using a training data set, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model mode a correct or incorrect prediction.
The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667.

Answer: D

Explanation:
The problem describes aclassification modelthat predicts whether a patient is at risk for a stroke. The confusion matrix is provided, and the testers have calculated a performance metric as2/3 or 0.6667.
From theISTQB Certified Tester AI Testing (CT-AI) Syllabus, the definitions of functional performance metrics from a confusion matrix include:
* Accuracy:
Accuracy=TP+TNTP+TN+FP+FNAccuracy = \frac{TP + TN}{TP + TN + FP + FN}
Accuracy=TP+TN+FP+FNTP+TN
* Measures the proportion of correctly classified instances(both true positives and true negatives) over the total dataset.
* If the value is0.6667, it suggests that the metric includesboth correct positive and negative classifications, aligning with accuracy.
* Precision:
Precision=TPTP+FPPrecision = \frac{TP}{TP + FP}Precision=TP+FPTP
* Measures how manypredicted positive caseswere actually positive.
* Doesnotmatch the given calculation.
* Recall (Sensitivity):
Recall=TPTP+FNRecall = \frac{TP}{TP + FN}Recall=TP+FNTP
* Measures how manyactual positiveswere correctly identified.
* Doesnotmatch the 0.6667 value.
* F1-Score:
F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{Precision \times Recall}{Precision + Recall} F1=2×Precision+RecallPrecision×Recall
* A balance between precision and recall.
* The formula isdifferent from the provided calculation.
Since the formula foraccuracymatches the calculated value of0.6667, the best answer isD. Accuracy.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 5.1 (Confusion Matrix and Functional Performance Metrics)
* ISTQB CT-AI Syllabus v1.0, Section 5.4 (Selecting ML Functional Performance Metrics)


NEW QUESTION # 100
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