Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?

Data for the use case is being generated at a fast pace.

Compliance needs require proper care to be taken of input personal data.

Nature of data constantly changes with lime.

Data for the use case is being generated at a fast pace.

Test data being sourced from public sources.

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

Natural language processing on textual requirements

Analyzing source code for generating test cases

Machine learning on logs of execution

GUI analysis by computer vision

The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.Which ONE of the following options BEST describes the inputs used to compute the activation value?

Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Individual bias at the neuron level, and weights assigned to the connections between the neurons.

Individual bias at the neuron level, and activation values of neurons in the previous layer.

Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?

Testing the speed of the training of the model.

Testing the accuracy of the classification model.

Testing the API of the service powered by the ML model.

Testing the speed of the training of the model.

Testing the speed of the prediction by the model.

Which ONE of the following describes a situation of back-to-back testing the LEAST?

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?

Challenges in the creation of scenarios of human handover for autonomous systems.

Challenges resulting from low accuracy of the models.

The challenge of mimicking undefined scenarios generated due to self-learning

The challenge of providing explainability to the decisions made by the system.

Challenges in the creation of scenarios of human handover for autonomous systems.

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?

Training data - validation data - test data

Training data - validation data

Which ONE of the following options BEST DESCRIBES clustering?

Clustering is done without prior knowledge of output classes.

Clustering is classification of a continuous quantity.

Clustering is supervised learning.

Clustering is done without prior knowledge of output classes.

Clustering requires you to know the classes.

Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?

A system that is fully able to respond to its environment.

A system that utilizes human beings for all important decisions.

A fully automated manufacturing plant that uses no software.

A system that utilizes a tool like Selenium.

A system that is fully able to respond to its environment.

Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?

Test for human handover when it should actually not be relinquishing control.

Test for human handover to give rest to the system.

Test for human handover when it should actually not be relinquishing control.

Test for human handover requiring mandatory relinquishing control.

Test for human handover after a given time interval.

'AllerEgo' is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real-world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?

The difficulty of defining criteria for improvement before the model can be accepted. Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.

The fast pace of change did not allow sufficient time for testing. This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.

The unknown nature and insufficient specification of the operating environment might have caused the poor performance. This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.

There was an algorithmic bias in the AI system. Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions. Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.

An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?

This is an example of sample bias.

This is an example of expert system bias.

This is an example of sample bias.

This is an example of hyperparameter bias.

This is an example of algorithmic bias.

In a conference on artificial intelligence (Al), a speaker made the statement, 'The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?

This statement is correct. In general, what is considered Al today may change over time.

This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.

This statement is correct. In general, what is considered Al today may change over time.

This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.

This statement is correct. In general, today the term Al is utilized incorrectly.

Which ONE of the following hardware is MOST suitable for implementing Al when using ML?

Hardware supporting fast matrix multiplication.

Hardware supporting high precision floating point operations.

Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.

These examples are model specific and are not likely to cause another model trained on same task to fail.

Black box attacks based on adversarial examples create an exact duplicate model of the original.

These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.

These attacks can't be prevented by retraining the model with these examples augmented to the training data.

These examples are model specific and are not likely to cause another model trained on same task to fail.

Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?

Using an Al-based tool to optimize the regression test suite by analyzing past test results

Identifying suitable tests by looking at the complexity of the test cases.

Using of a random subset of tests.

Automating test scripts using Al-based test automation tools.

Using an Al-based tool to optimize the regression test suite by analyzing past test results

Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

ML model metrics to evaluate the functional performance

Different features like ADAS, Lane Change Assistance etc.

Which ONE of the following statements correctly describes the importance of flexibility for Al systems?

Flexible Al systems allow for easier modification of the system as a whole.

Al systems are inherently flexible.

Al systems require changing of operational environments; therefore, flexibility is required.

Flexible Al systems allow for easier modification of the system as a whole.

Self-learning systems are expected to deal with new situations without explicitly having to program for it.

Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?

Test the model during model evaluation for data bias.

Testing the distribution shift in the training data for inappropriate bias.

Test the model during model evaluation for data bias.

Testing the data pipeline for any sources for algorithmic bias.

Check the input test data for potential sample bias.

Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?

Using a classification model to predict the presence of a defect by using code quality metrics as the input data.

Identifying the relationship between developers and the modules developed by them.

Search of similar code based on natural language processing.

Clustering of similar code modules to predict based on similarity.

Using a classification model to predict the presence of a defect by using code quality metrics as the input data.

Which ONE of the following options describes a scenario of A/B testing the LEAST?

A comparison of the performance of an ML system on two different input datasets.

A comparison of two different websites for the same company to observe from a user acceptance perspective.

A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.

A comparison of the performance of an ML system on two different input datasets.

A comparison of the performance of two different ML implementations on the same input data.

''BioSearch'' is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?

A lack of similarity between the training and testing data.

The input data has not been tested for quality prior to use for testing.

A lack of focus on choosing the right functional-performance metrics.

A lack of focus on non-functional requirements testing.

A ML engineer is trying to determine the correctness of the new open-source implementation *X', of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?

Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.

Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

'Splendid Healthcare' has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.Which ONE of the following combinations requires MAXIMIZATION?

Maximize precision and accuracy

Maximize specificity number of classes

Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?

Using a pixel comparison of the GUI before and after the change to check the differences.

Using a computer vision to compare the GUI before and after the test object changes.

Using a vision-based detection of the GUI layout changes before and after test object changes.

Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.

iSQI CT-AI Practice Test 1 - Free Online Exam Prep & Questions

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Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.

Which ONE of the following factors is NOT a factor affecting the ML functional performance?

The data pipeline

The quality of the labeling

Biased data

The number of classes

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