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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

A. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
B. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
C. These examples are model specific and are not likely to cause another model trained on same task to fail.
D. Black box attacks based on adversarial examples create an exact duplicate model of the original.

Answer: C

Explanation:
* A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
* Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
* B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
* Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
* C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
* This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
* D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
* Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.

NEW QUESTION # 38
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Robustness
B. Self-learning
C. High complexity
D. Non-determinism

Answer: A

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
References:
* ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 39
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?
SELECT ONE OPTION

A. 1,0.9, 0.8
B. 0.84.1,0.9
C. 1,0.87,0.84
D. 0.87.0.9. 0.84

Answer: D

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+FNtext{Accuracy} = frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN
* Calculation: Accuracy=45+4245+42+8+5=87100=0.87text{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+FNtext{Recall} = frac{TP}{TP + FN}Recall=TP+FNTP
* Calculation: Recall=4545+5=4550=0.9text{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+FPtext{Specificity} = frac{TN}{TN + FP}Specificity=TN+FPTN
* Calculation: Specificity=4242+8=4250=0.84text{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.
References:
* ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
* "ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

NEW QUESTION # 40
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?
SELECT ONE OPTION

A. Regression
B. Clustering
C. Association
D. Classification

Answer: B

Explanation:
* A. Regression
* Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
* B. Association
* Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
* C. Clustering
* 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.
* D. Classification
* Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.

NEW QUESTION # 41
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A. A system that utilizes a tool like Selenium.
B. A fully automated manufacturing plant that uses no software.
C. A system that is fully able to respond to its environment.
D. A system that utilizes human beings for all important decisions.

Answer: C

Explanation:
AI-Based Autonomous Functions:An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

NEW QUESTION # 42
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