CT-AI 試験問題を無料オンラインアクセス

A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop-off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

• A.The robot must record the time of each delivery which is compiled into a report
• B.The robot must recharge for no more than six hours a day
• C.The robot must complete 99.99% of its deliveries each day
• D.The robot must evolve to optimize its routing

Which of the following problems would best be solved using the supervised learning category of regression?

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

Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

• A.The tester wishes to better understand the logic of the software used to create the internal structure
• B.The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
• C.Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
• D.Test automation can be built quickly and easily from the test cases developed during black-box testing

There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

• A.Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers
• B.Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
• C.Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
• D.Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes

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.90.0%
• B.98.9%
• C.94.5%
• D.94.2%