Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem? SELECT ONE OPTION
- A . Security issues
- B . Accuracy issues
- C . Privacy issues
- D . Bias issues
B
Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues."
Here’s a detailed explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model’s accuracy, leading to poor performance because the model learns incorrect patterns.
Why Not Other Options:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.
Reference: This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models.
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
- A . Natural language processing on textual requirements
- B . Analyzing source code for generating test cases
- C . Machine learning on logs of execution
- D . GUI analysis by computer vision
A
Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP).
Here’s why:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn’t apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
Reference: This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.
A software component uses machine learning to recognize the digits from a scan of handwritten numbers.
In the scenario above, which type of Machine Learning (ML) is this an example of? SELECT ONE OPTION
- A . Reinforcement learning
- B . Regression
- C . Classification
- D . Clustering
C
Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification.
Here’s a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
Reference: The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.
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
- A . Aspects II, III and IV
- B . Aspects I, II, and III
- C . Aspects III, IV, and V
- D . Aspects I, IV, and V
C
Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here’s why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
Reference: This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al? SELECT ONE OPTION
- A . Search engines
- B . Procedural programming
- C . Case control structures
- D . Genetic algorithms
D
Explanation:
Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI. They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.
Which ONE of the following approaches to labelling requires the least time and effort? SELECT ONE OPTION
- A . Outsourced
- B . Pre-labeled dataset
- C . Internal
- D . Al-Assisted
B
Explanation:
Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock. SELECT ONE OPTION
- A . Testing for accuracy
- B . Testing for bias
- C . Testing for concept drift
- D . Testing for security
C
Explanation:
Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.
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
- A . Value coverage
- B . Threshold coverage
- C . Sign change coverage
- D . Neuron coverage
C
Explanation:
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
Which ONE of the following describes a situation of back-to-back testing the LEAST? SELECT ONE OPTION
- 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.
- 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 same data
- C . Comparison of the results of a neural network ML model with a current decision tree ML model for 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.
C
Explanation:
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:
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
- A . Challenges resulting from low accuracy of the models.
- B . The challenge of mimicking undefined scenarios generated due to self-learning
- C . The challenge of providing explainability to the decisions made by the system.
- D . Challenges in the creation of scenarios of human handover for autonomous systems.
D
Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments.
Let’s evaluate each option: