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CT-AI Actual Real Questions: Certified Tester AI Testing Exam & CT-AI Practice Questions

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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

systems from those required for conventional systems.

Topic 2

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 3

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 4

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 5

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 6

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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

A. Insufficient function: the model was not trained to check for colors or words
B. Inherited bias: the model could have inherited unknown defects
C. There is no risk, as the model has already been trained
D. Improper data preparation

Answer: B

Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

NEW QUESTION # 74
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining if an animal is a pig or a cow based on image recognition
B. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store
C. Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner
D. 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

Answer: D

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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

NEW QUESTION # 75
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?

A. A/B testing
B. Metamorphic testing
C. Pairwise testing
D. Exploratory testing

Answer: A

Explanation:
A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, the new algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.
A/B testing is commonly used in:
* User experience testing(e.g., comparing different versions of a website).
* ML model evaluation(e.g., comparing two AI-based classifiers).
* Performance assessment(e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.
* Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 76
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

A. Testing the distribution shift in the training data for inappropriate bias.
B. Test the model during model evaluation for data bias.
C. Testing the data pipeline for any sources for algorithmic bias.
D. Check the input test data for potential sample bias.

Answer: B

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 # 77
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Machine learning on logs of execution
B. GUI analysis by computer vision
C. Analyzing source code for generating test cases
D. Natural language processing on textual requirements

Answer: D

Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

NEW QUESTION # 78
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