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NEW QUESTION # 94
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Answer: C
Explanation:
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 95
Which ONE of the below is MOST likely to be a challenge in ML data preparation?
Answer: D
Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 96
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely-including noise and irrelevant detail-resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involves transfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.
NEW QUESTION # 97
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
Answer: A
Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.
NEW QUESTION # 98
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION
Answer: A
Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.
NEW QUESTION # 99
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