Latest CT-AI_v1.0_World exam dumps with real ISQI questions and answers [Q19-Q37]

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Latest CT-AI_v1.0_World exam dumps with real ISQI questions and answers

CT-AI_v1.0_World Exam in First Attempt Guaranteed

Q19. Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

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.

Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let’s analyze the options given:
* A. 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.
* This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
* B. 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 the same data.
* This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
* C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
* This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
* D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
* This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn’t involve comparing multiple implementations.
Based on this analysis, optionCis the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

Q20. Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

Natural language processing on textual requirements

Analyzing source code for generating test cases

Machine learning on logs of execution

GUI analysis by computer vision

Q21. Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

Training data – validation data – test data

Training data – validation data

Training data * test data

Validation data – test data

Q22. 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?
SELECT ONE OPTION

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.

Q23. Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

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 tothe training data.

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

Q24. 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?
SELECT ONE OPTION

The data pipeline

The quality of the labeling

Biased data

The number of classes

Q25. Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

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.

A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here’s why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* “Understanding A/B Testing” (ISTQB CT-AI Syllabus).

Q26. 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?
SELECT ONE OPTION

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.

Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let’s break down why option D is the correct choice:
* Understanding Classification Models:
* Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either “defective” or “non-defective” based on the input features.
* Input Data – Code Quality Metrics:
* The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects.
* Historical Data:
* Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
* Why Option D is Correct:
* Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
* Eliminating Other Options:
* A. Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
* B. Search of similar code based on natural language processing: While useful for other
* purposes, this method does not describe defect prediction using classification models and code metrics.
* C. Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
* ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
* “Using AI for Defect Prediction” (ISTQB CT-AI Syllabus, Section 11.5.1).

Q27. 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?
SELECT ONE OPTION

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.

Q28. Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

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.

Q29. Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

Evaluating the model

Deploying the model

Tuning the model

Data testing

Q30. Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

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.

Q31. Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

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.

Q32. Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I – V).
I.Autonomy
II.Maintainability
III.Safety
IV.Transparency
V.Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

Aspects II, III and IV

Aspects I, II, and III

Aspects III, IV, and V

Aspects I, IV, and V

Q33. 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?
SELECT ONE OPTION

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.

Q34. Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

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.