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| Section | Objectives |
|---|---|
| Topic 1: AI System Lifecycle and Operations | - Deployment and monitoring
|
| Topic 2: AI Quality Characteristics | - Quality attributes
|
| Topic 3: Data Quality and Bias | - Data quality assurance
|
| Topic 4: Testing AI-Based Systems | - Test design techniques
|
| Topic 5: Machine Learning Fundamentals for Testing | - Model types
|
| Topic 6: Introduction to AI Testing | - Challenges in AI testing
|
| Topic 7: Ethics and Risk in AI Testing | - Risk-based testing for AI
|
Once the user has used our CT-AI learning material for a mock exercise, the product's system automatically remembers and analyzes all the user's actual operations. The user must complete the test within the time specified by the simulation system, and there is a timer on the right side of the screen, as long as the user begins the practice of CT-AI Learning Materials, the timer will run automatic and start counting.
NEW QUESTION # 18
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
Answer: D
Explanation:
When testing an AI-based chat assistant for e-commerce customers, the lack of sufficient specifications makes it difficult to use structured test techniques. The ISTQB CT-AI Syllabus recommends exploratory testing in such cases:
Exploratory testing is useful when specifications are incomplete or unclear. AI-based systems, particularly those using natural language processing (NLP), may not behave deterministically, making scripted test cases ineffective.
The tester interacts dynamically with the system, identifying unexpected behaviors not documented in the specification.
NEW QUESTION # 19
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?
Answer: A
Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.
NEW QUESTION # 20
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are.
Which is the MOST likely metric they should use?
Answer: D
Explanation:
The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.
NEW QUESTION # 21
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?
Answer: A
Explanation:
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 22
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?
Answer: B
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 # 23
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