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CT-AI Test Dates - CT-AI Lab Questions

P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by Pass4cram: https://drive.google.com/open?id=1o82vgAb8vK4X_RFaBt6g3LzM6s6Nijdd

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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

Topic 4

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 5

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 6

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

Topic 7

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.

>> CT-AI Test Dates <<

CT-AI Lab Questions, Pass Leader CT-AI Dumps

The ISTQB CT-AI certification brings multiple career benefits. Reputed firms happily hire you for well-paid jobs when you earn the Certified Tester AI Testing Exam. If you are already an employee of a tech company, you get promotions and salary hikes upon getting the CT-AI credential. All these career benefits come when you crack the ISTQB CT-AI certification examination. To pass the CT-AI test, you need to prepare well from updated practice material such as real ISTQB CT-AI Exam Questions.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)

A. Determinism
B. Complexity
C. Interpretability
D. Robustness

Answer: B

Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness. Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism does notmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.

NEW QUESTION # 37
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

A. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
B. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
C. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

Answer: B

Explanation:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:
A . Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B . Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C . Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D . Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

NEW QUESTION # 38
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for bias
B. Testing for accuracy
C. Testing for security
D. Testing for concept drift

Answer: D

Explanation:
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

NEW QUESTION # 39
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure 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. 98.9%
B. 90.0%
C. 94.5%
D. 94.2%

Answer: C

Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)

NEW QUESTION # 40
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Choose ONE option (1 out of 4)

A. F1 value
B. -
C. Sensitivity
D. Accuracy

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non- fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor.
Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.
TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:
* Precision protects legitimate customers by minimizing false alarms.
* Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.

NEW QUESTION # 41
......

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CT-AI Lab Questions: https://www.pass4cram.com/CT-AI_free-download.html

P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by Pass4cram: https://drive.google.com/open?id=1o82vgAb8vK4X_RFaBt6g3LzM6s6Nijdd

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