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| Section | Objectives |
|---|---|
| Topic 1: AI Quality Characteristics | - Quality attributes
|
| Topic 2: Machine Learning Fundamentals for Testing | - ML lifecycle
|
| Topic 3: Data Quality and Bias | - Data quality assurance
|
| Topic 4: AI System Lifecycle and Operations | - Continuous learning systems
|
| Topic 5: Testing AI-Based Systems | - Test design techniques
|
| Topic 6: Ethics and Risk in AI Testing | - Ethical considerations
|
| Topic 7: Introduction to AI Testing | - AI systems overview
|
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NEW QUESTION # 154
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?
Answer: C
Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
NEW QUESTION # 155
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
Answer: A
Explanation:
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)
NEW QUESTION # 156
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke. The model collects information on each patient regarding their blood pressure, red blood cell count, smoking, status, history of heart disease, cholesterol level, and demographics.
Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. One the model is created using a training data set, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model mode a correct or incorrect prediction. The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667. Which metric did the testers calculate?
Answer: B
Explanation:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
NEW QUESTION # 157
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: A
Explanation:
The correct answer is A . Dynamic testing for inappropriate bias may require testers to obtain additional attributes about the input data so that outcomes can be analyzed across relevant groups. The CT-AI syllabus explains that testers may need additional information about attributes potentially related to bias and then correlate those attributes with system results. This is particularly important where bias is based on "hidden" variables that are not explicitly present in the input data but are inferred by the algorithm.
Option B is partly relevant to static testing or review activity, but the question asks specifically about dynamic testing . Option C is incorrect because inappropriate bias may be present even when protected attributes are not explicit model inputs; the model may infer them indirectly from proxy variables. Option D is also incorrect because the syllabus states that measuring how changes in inputs affect outputs for bias-related groups may be performed in production as well as before release. Therefore, obtaining and correlating additional attributes is a valid and often necessary dynamic testing activity.
References/topics: CT-AI Syllabus Chapter 8, Section 8.3 "Testing for Algorithmic, Sample and Inappropriate Bias."
=========
NEW QUESTION # 158
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: D
Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful.
Therefore, the operational model should be regularly evaluated against its acceptance criteria."
NEW QUESTION # 159
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