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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Passing Score:	65%
Exam Price:	EUR 250
Available Languages:	English
Exam Duration:	60 minutes
Exam Format:	Multiple Choice
Certificate Validity Period:	Lifetime (no expiration)
Real Exam Qty:	40
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

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Latest CT-AI Braindumps Sheet, Reliable CT-AI Exam Bootcamp

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
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	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	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 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

ISTQB Certified Tester AI Testing Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

A. Test data being sourced from public sources.
B. Data for the use case is being generated at a fast pace.
C. Nature of data constantly changes with lime.
D. Compliance needs require proper care to be taken of input personal data.

Answer: B

Explanation:
* Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.

NEW QUESTION # 102
Which statement regarding AI for defect prediction is correct?

A. AI-based defect prediction is based on formal principles and requires only a few factors.
B. AI-based defect prediction is most effective when based on previous similar constellations.
C. AI-based defect prediction is most effective when based on source-code metrics such as branches or McCabe complexity.
D. AI-based defect prediction can detect whether defects exist but not where.

Answer: B

Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.

NEW QUESTION # 103
Which of the following problems would best be solved using the supervised learning category of regression?

A. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
B. Determining if an animal is a pig or a cow based on image recognition.
C. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.
D. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.

Answer: C

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."

NEW QUESTION # 104
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

A. Aspects II, III and IV
B. Aspects I, II, and III
C. Aspects III, IV, and V
D. Aspects I, IV, and V

Answer: C

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.

NEW QUESTION # 105
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. An old application with lots of defect records but a brand new development and test team
B. A new application which is in the early stages of the first test cycle
C. An old application where defect records are linked to failed tests and production incidents
D. An old application with few critical defect records and many non-critical defect records

Answer: C

Explanation:
An old application where defect records are linked to failed tests and production incidents would provide the most valuable data for an ML model to determine the criticality of new defects. By using historical data of defects that are linked to actual issues in production or testing failures, the model can learn patterns and correlations between defects and their criticality, making it highly effective in predicting the criticality of new defects. This type of historical data provides the necessary context for accurate predictions.

NEW QUESTION # 106
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