A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III): I.Pairwise testing of combinations
II.Testing each individual model for accuracy III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
Correct Answer:
B
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
✑ Pairwise testing of combinations (I): This method is useful for testing interactions
between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
✑ Testing each individual model for accuracy (II): Ensuring that each model in the
workflow performs accurately on its own is crucial before integrating them into a combined workflow.
✑ A/B testing of different sequences of models (III): This involves comparing different
sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
✑ ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
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
Correct Answer:
A
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:
References: This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION
Correct Answer:
B
✑ A. 64-bit CPUs.
✑ B. Hardware supporting fast matrix multiplication.
✑ C. High powered CPUs.
✑ D. Hardware supporting high precision floating point operations.
Therefore, the correct answer is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION
Correct Answer:
B
✑ A. Feature selection
✑ B. Data sampling
✑ C. Data labeling
✑ D. Data augmentation
Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.
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
Correct Answer:
D
Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.
