ISTQB Updated CT-AI Exam Questions and Answers by montague

 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​​.

Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is toprepare and pre-process the databefore training the model.

Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models​.

The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering​.

Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.

A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated​.

C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection​.

D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation​.

ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)

"Data preparation comprises data acquisition, pre-processing, and feature engineering. Exploratory data analysis (EDA) may be performed alongside these activities"​.

"The data used to train, tune, and test the model must be representative of the operational data that will be used by the model"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.

Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.

Supervised Learning Process

The algorithm learns from labeled data, mapping inputs to correct outputs during training.

If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.

Quality of Training Data

The accuracy of any supervised ML model ishighly dependent on the quality of labels.

Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.

Error Minimization and Model Accuracy

Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.

It leads to overfitting or underfitting, which decreases the model's ability to generalize.

Industry Standard Practices

Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.

(B) Minimizing the amount of time spent training the algorithm❌(Incorrect)

While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.

(C) Selecting the correct data pipeline for the ML training❌(Incorrect)

A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.

(D) Grouping similar products together before feeding them into the algorithm❌(Incorrect)

This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.

Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."​

Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly."​

Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.

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.