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| Section | Objectives |
|---|---|
| Topic 1: Introduction to AI Testing | - AI systems overview
|
| Topic 2: Testing AI-Based Systems | - Test levels for AI systems
|
| Topic 3: Ethics and Risk in AI Testing | - Risk-based testing for AI
|
| Topic 4: AI Quality Characteristics | - Quality attributes
|
| Topic 5: AI System Lifecycle and Operations | - Continuous learning systems
|
| Topic 6: Machine Learning Fundamentals for Testing | - ML lifecycle
|
| Topic 7: Data Quality and Bias | - Data quality assurance
|
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NEW QUESTION # 43
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are. Which is the MOST likely metric they should use:
Answer: B
Explanation:
The correct answer is B. Intra cluster . The scenario describes an unsupervised clustering model, where the objective is to group data points based on similarity. The requested measurement is specifically how similar the data points are within each group , which maps directly to intra-cluster metrics . The CT-AI syllabus states that inter-cluster metrics, intra-cluster metrics, and the silhouette coefficient may be used for unsupervised clustering problems.
Option A, ROC, and option C, AUC, are used for supervised classification, especially binary classifiers, to evaluate how well the classifier distinguishes between classes. Option D, recall, is also a supervised classification metric and measures how many actual positives were correctly predicted. None of those metrics is designed to evaluate compactness or similarity within clusters. Intra-cluster measurement is the most direct metric family because it evaluates the cohesion of a cluster: the closer or more similar the members are, the stronger the clustering result is likely to be.
References/topics: CT-AI Syllabus Chapter 5, Section 5.2 "Additional ML Functional Performance Metrics for Classification, Regression and Clustering"; Section 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 44
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:
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
NEW QUESTION # 45
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?
Answer: A
Explanation:
Tools may be required to simulate extreme scenarios because autonomous systems often need to be tested under challenging or rare conditions that are difficult to replicate in a real-world environment. These scenarios, such as extreme weather, system failures, or unexpected behaviors, need to be simulated in a controlled environment to ensure the system can handle a wide range of situations safely and effectively. This makes the test environment for autonomous systems unique compared to other types of testing.
NEW QUESTION # 46
Which statement describes factors related to test data that make testing AI-based systems difficult?
Answer: C
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 # 47
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.
For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
Answer: A
Explanation:
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
Confusion Matrix:
Actually Rotten: 45 (True Positive), 8 (False Positive)
Actually Fresh: 5 (False Negative), 42 (True Negative)
Accuracy:
Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
Formula: Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN Calculation: Accuracy=45+4245+42+8+5=87100=0.87\text{Accuracy} = \frac{45 + 42}{45 + 42 + 8
+ 5} = \frac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87
Recall (Sensitivity):
Recall is the proportion of true positive results in the total actual positives.
Formula: Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}Recall=TP+FNTP Calculation: Recall=4545+5=4550=0.9\text{Recall} = \frac{45}{45 + 5} = \frac{45}{50} =
0.9Recall=45+545=5045=0.9
Specificity:
Specificity is the proportion of true negative results in the total actual negatives.
Formula: Specificity=TNTN+FP\text{Specificity} = \frac{TN}{TN + FP}Specificity=TN+FPTN Calculation: Specificity=4242+8=4250=0.84\text{Specificity} = \frac{42}{42 + 8} = \frac{42}{50} =
0.84Specificity=42+842=5042=0.84
Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
NEW QUESTION # 48
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