[Nov 04, 2024] New CT-AI_v1.0_World Exam Dumps with High Passing Rate [Q20-Q35]

NEW QUESTION 20
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

Value coverage

Threshold coverage

Sign change coverage

Neuron coverage

NEW QUESTION 21
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.

NEW QUESTION 22
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.

NEW QUESTION 23
“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?
SELECT ONE OPTION

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.

NEW QUESTION 24
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.

NEW QUESTION 25
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

Outsourced

Pre-labeled dataset

Internal

Al-Assisted

NEW QUESTION 26
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.

NEW QUESTION 27
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

Search engines

Procedural programming

Case control structures

Genetic algorithms

NEW QUESTION 28
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.

NEW QUESTION 29
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

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.

NEW QUESTION 30
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).

NEW QUESTION 31
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

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.

NEW QUESTION 32
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?
SELECT ONE OPTION

Different Road Types

Different weather conditions

ML model metrics to evaluate the functional performance

Different features like ADAS, Lane Change Assistance etc.

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
* Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car’s system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
* Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car’s sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
* ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
* Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
* Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

NEW QUESTION 33
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.

NEW QUESTION 34
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

NEW QUESTION 35
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

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.

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