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| Section | Objectives |
|---|---|
| Topic 1: Testing AI-Based Systems | - Test levels for AI systems
|
| Topic 2: AI Quality Characteristics | - Quality attributes
|
| Topic 3: AI System Lifecycle and Operations | - Deployment and monitoring
|
| Topic 4: Introduction to AI Testing | - Challenges in AI testing
|
| Topic 5: Data Quality and Bias | - Data quality assurance
|
| Topic 6: Machine Learning Fundamentals for Testing | - ML lifecycle
|
| Topic 7: Ethics and Risk in AI Testing | - Risk-based testing for AI
|
CT-AI is so flexible that you can easily change the timings, types of questions, and topics for each mock exam. Actual4Cert's Certified Tester AI Testing Exam practice test contains all the important questions that will appear in the actual CT-AI Exam. We design and update our ISTQB CT-AI exam questions after receiving precious feedback. You can try a demo and sample of CT-AI exam questions before purchasing.
NEW QUESTION # 83
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
Answer: A
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
:
ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
NEW QUESTION # 84
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
Answer: A
Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A). Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B). The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C). The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D). Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.
NEW QUESTION # 85
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that a single-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.
NEW QUESTION # 86
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?
Answer: B
Explanation:
The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
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NEW QUESTION # 87
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
Answer: A
Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.
NEW QUESTION # 88
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