AI Implementation in System Testing

Which of the following best defines "AI implementation" in the context of system testing?

• The process of writing code for a new software application

• The integration and validation of artificial intelligence components within a system to ensure correct functionality

• The installation of hardware components in a computer system

• The documentation of user requirements for a system

What is the primary goal of system testing when implementing AI in a system?

• To check the spelling in the user manual

• To verify that the AI component works as intended within the entire system

• To design the user interface

• To create training data for the AI model

Which term describes the process of feeding test data to an AI model to evaluate its performance during system testing?

• Training

• Inference

• Validation

• Deployment

Which of the following is a simple procedure commonly used in system testing of AI implementations?

• Running unit tests on individual AI functions

• Writing a research paper on AI ethics

• Designing a new AI algorithm

• Creating a business plan for AI adoption

When testing an AI system, which concept involves checking if the AI model generalizes well to unseen data?

• Overfitting

• Underfitting

• Generalization

• Regularization

Which of the following best describes a black-box testing approach for AI in system testing?

• Testing the internal code structure of the AI model

• Evaluating the AI system based on its outputs without knowledge of its internal workings

• Modifying the AI algorithm during testing

• Debugging the source code of the AI component

Suppose you are tasked with testing an AI-based recommendation system. Which skill is most relevant for designing test cases that cover different user scenarios?

• Memorizing the system requirements

• Applying knowledge of user behavior patterns to create diverse test inputs

• Writing code for the recommendation algorithm

• Installing the recommendation system on a server

During system testing of an AI implementation, which concept is used to measure the accuracy of the AI model's predictions?

• Precision and recall

• Data normalization

• Feature engineering

• Model training

You are planning a system test for an AI-powered fraud detection system. Which strategy would best ensure that the system can handle real-world scenarios?

• Test only with synthetic data generated in the lab

• Use a combination of historical fraud cases and simulated new cases to evaluate the system's robustness

• Focus solely on the speed of the AI algorithm

• Ignore edge cases in the test plan

A system test reveals that an AI model performs well on most data but fails on certain edge cases. What is the most strategic next step?

• Ignore the edge cases and deploy the system

• Analyze the failed cases, gather evidence, and plan targeted improvements to the AI model

• Retrain the model with the same data

• Remove the AI component from the system