Which of the following are the three activities in the data acquisition activities for data preparation?

Identifying, gathering, labelling

Cleaning, transforming, augmenting

Feature selecting, feature growing, feature augmenting

Identifying, gathering, labelling

A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.How could EDA be used to detect this attack?

EDA can help detect the outlier emails from the real emails.

EDA can detect and remove the false emails.

EDA can restrict how many inputs can be provided by unique users.

EDA cannot be used to detect the attack.

An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.Which of the following statements BEST describes the problem and how it could have been prevented?

The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.

The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed.

The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes.

The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated.

Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.

It has been trained to recognize cats and is given an image of a dog.

It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.

It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.

It has been trained to analyze customer buying trend data and is given information on supplier cost data.

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?

Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.

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.

Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.

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.

Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.

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

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.

Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.

Determining if an animal is a pig or a cow based on image recognition.

Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.

Which of the following is a problem with AI-generated test cases that are generated from the requirements?

They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.

They are slow and will usually not be able to execute in the time allowed.

They are defect prone because they are unable to detect nuances in the requirements.

They make debugging more complicated because the number of steps is usually high in order to induce the target failure.

They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.

Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.

Run the test several times to ensure that the AI always returns the same correct test result.

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.

Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.

Which of the following is an example of overfitting?

The model is not able to generalize to accommodate new types of data.

The model is too simplistic for the data.

The model is missing relationships between the inputs and outputs.

The model discards data it considers to be noise or outliers.

You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.How could AI help make the automation more reliable?

It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

It could modify the automation code to ignore unrecognizable objects to avoid failures.

It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.

It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.

An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

Minimizing the amount of time spent training the algorithm

Selecting the correct data pipeline for the ML training

Grouping similar products together before feeding them into the algorithm

A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.Which of the following is the most likely cause of the failure?

The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.

The feedback requires a physical connection and cannot be sent over the Internet.

Mobile operating systems cannot process machine learning algorithms.

The size of the application is consuming too much of the phone's storage capacity.

The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.

The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.What should be the next step to progress along the machine learning workflow?

Tune the machine learning algorithm based on objectives and business priorities

Prepare and pre-process the data that will be used to train and test the model

Agree on defined acceptance criteria for the machine learning model

Evaluate the selection of the framework and the model

A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.What test method should you use to verify that the model has improved after the additional training?

Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.

Metamorphic testing because the application domain is not clearly understood at this point.

Adversarial testing to verify that no incorrect images have been used in the training.

Pairwise testing using combinatorics to look at a long list of photo parameters.

Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.

You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.

The number of parameters to test can be reduced to less than a dozen.

All high priority defects will be identified using this method.

While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.

Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.

Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

Associating shoppers with their shopping tendencies

Grouping individual fish together based on their types of fins

Classifying muffin purchases based on the perceived attractiveness of their packaging

Estimating the expected purchase of cat food after a particularly successful ad campaign

Which of the following aspects is a challenge when handling test data for an AI-based system?

Personal data or confidential data

Output data or intermediate data

Video frame speed or aspect ratio

Data frameworks or machine learning frameworks

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?

By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases

By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases

By performing bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases

By using A/B testing to compare the last update with the newest change and compare metrics between the two

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?

The robot must evolve to optimize its routing

The robot must recharge for no more than six hours a day

The robot must record the time of each delivery which is compiled into a report

The robot must complete 99.99% of its deliveries each day

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?

Inherited bias: the model could have inherited unknown defects

There is no risk, as the model has already been trained

Insufficient function; the model was not trained to check for colors or words

Inherited bias: the model could have inherited unknown defects

When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

Test cases to detect the system prompting for unnecessary human intervention

Test cases to verify that the system automatically confirms the correct classification of training data

Test cases to detect the system appropriately automating its data input

Test cases to detect the system prompting for unnecessary human intervention

Test cases to verify that the system automatically suppresses invalid output data

Which of the following is correct regarding the layers of a deep neural network?

There is at least one internal hidden layer

There is only an input and output layer

There is at least one internal hidden layer

There must be a minimum of five total layers to be considered deep

The output layer is not connected with the other layers to maintain integrity

A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.Which of the following describes the next phase of metamorphic testing?

The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.

The team decomposes each route into the relevant components that affect the travel time such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.

The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.

The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.

A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not. The bank has enough data on past customers to randomly split the data into a training data set and a test/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:GenderMarital statusNumber of dependentsEducationIncomeLoan amountLoan termCredit scoreThe model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.Given this information, what is the best test approach to check for potential bias in the model?

Experienced-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set.

Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set, if the two models significantly differ, it will indicate there is bias in the original model.

Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.

A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.

An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

Selecting training data by purposely excluding specific faulty conditions

Selecting training data by purposely including all known faulty conditions

Selecting testing data from a different dataset than the training dataset

Selecting testing data from a boat manufacturer's bolt longevity data

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?

Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.

Test automation can be built quickly and easily from the test cases developed during black-box testing.

The tester wishes to better understand the logic of the software used to create the internal structure.

The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure.

Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.

iSQI CT-AI Practice Test 2 - Free Online Exam Prep & Questions

Question 1 / 40

A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: 'I don't understand your question.'

This AI system should be categorized as?

General AI

Narrow AI

Super AI

Conventional AI

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