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| Section | Objectives |
|---|---|
| Topic 1: Testing AI-Based Systems | - Test levels for AI systems
|
| Topic 2: Machine Learning Fundamentals for Testing | - ML lifecycle
|
| Topic 3: AI Quality Characteristics | - Quality attributes
|
| Topic 4: Introduction to AI Testing | - AI systems overview
|
| Topic 5: Ethics and Risk in AI Testing | - Risk-based testing for AI
|
| Topic 6: Data Quality and Bias | - Bias and fairness
|
| Topic 7: AI System Lifecycle and Operations | - Continuous learning systems
|
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NEW QUESTION # 96
Which statement describes factors related to test data that make testing AI-based systems difficult?
Answer: A
Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.
NEW QUESTION # 97
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below. What is the precision of this predictive model
Answer: D
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
TP = 90
FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100% = 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.
NEW QUESTION # 98
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI based systems?
Answer: D
Explanation:
The correct answer is C . The CT-AI syllabus explains that AI-based systems can have extremely high numbers of parameters, especially where they use big data or interact with the external world. Exhaustive testing of all parameter combinations is usually infeasible, so combinatorial techniques, especially pairwise testing, can reduce the number of required test cases while preserving useful defect detection capability.
The syllabus gives autonomous driving as an example, where high-level system tests should consider different operational environments and vehicle functions. Relevant parameters may include road types, surfaces, weather, traffic conditions, visibility, vehicle functions, and sensor effectiveness. This directly supports treating system inputs and environmental factors as parameters in pairwise tests.
Option A is not the intended application; neuron coverage is a neural-network coverage topic, not pairwise system scenario design. Option B describes A/B testing or back-to-back comparison, not combinatorial testing. Option D is false because the syllabus explicitly describes pairwise testing for AI-based systems and includes it as an examinable method.
References/topics: CT-AI Syllabus Chapter 9, Section 9.2 "Pairwise Testing."
=========
NEW QUESTION # 99
Which of the following problems would best be solved using the supervised learning category of regression?
Answer: D
Explanation:
Understanding Supervised Learning - RegressionSupervised learning is a category of machine learning where the model is trained on labeled data. Within this category,regressionis used when the goal is to predict a continuous numeric value.
* Regressiondeals with problems where the output variable is continuous in nature, meaning it can take any numerical value within a range.
* Common examples include predicting prices, estimating demand, and analyzing production trends.
* (A) Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens.#(Correct)
* This is a classicregression problembecause it involves predicting a continuous variable:daily egg productionbased on the input variablechicken's age.
* The goal is to find a numerical relationship between age and egg production, which makesregression the appropriate supervised learning method.
* (B) Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner.#(Incorrect)
* This is animage recognition task, which falls underclassification, not regression.
* Classification problems involve assigning inputs to discrete categories (e.g., "knife detected" or
"no knife detected").
* (C) Determining if an animal is a pig or a cow based on image recognition.#(Incorrect)
* This is anotherclassification problemwhere the goal is to categorize an image into one of two labels (pig or cow).
* (D) Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.#(Incorrect)
* This problem could involve a mix ofclassificationandassociation rule learning, but it does not explicitly predict a continuous variable in the way regression does.
* Regression is used when predicting a numeric output."Predicting the age of a person based on input data about their habits or predicting the future prices of stocks are examples of problems that use regression."
* Supervised learning problems are divided into classification and regression."If the output is numeric and continuous in nature, it may be regression."
* Regression is commonly used for predicting numerical trends over time."Regression models result in a numerical or continuous output value for a given input." Analysis of Answer ChoicesReferences from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles of regression-based supervised learning.
NEW QUESTION # 100
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
Answer: C
Explanation:
* A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
* B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
* Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
* C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
* This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
* D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, optionCis the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.
NEW QUESTION # 101
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