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NEW QUESTION # 91
Which ONE of the below types of testing is NOT a type of experience-based testing applied to an AI-based system?
Answer: D
Explanation:
Using an industry checklist to assess the steps used to prepare an ML system is not typically considered a type of experience-based testing. Experience-based testing is based on the tester's expertise and knowledge of the system, such as exploring training data, conducting usability testing, and identifying potential biases based on past experiences with ML systems. Using a checklist is more of a procedural or guideline-based approach rather than one based on experiential insight.
NEW QUESTION # 92
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
Answer: C
Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
NEW QUESTION # 93
Which supervised-learning classification/regression statement is correct?
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches. It defines classification as predicting categorical labels, where as regression predicts continuous numerical values. OptionB--deciding whether an object is a bicycle or a motorcycle-- fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation.
NEW QUESTION # 94
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?
Answer: A
Explanation:
Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.
* Detecting Outliers:
* EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).
* Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.
* Identifying Distribution Shifts:
* By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.
* If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.
* Feature Analysis for Adversarial Patterns:
* EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.
* Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.
* Counteracting Adversarial Attacks:
* Once anomalies are identified, the spam filter's detection rules can be improved by retraining the model on corrected datasets.
* The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.
* Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks."EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data."
* EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts.
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."
* EDA helps validate ML models and detect potential vulnerabilities."The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise." References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.
NEW QUESTION # 95
An Al based website will recommend holiday rental accommodation for a user based on their previous bookings, and inputs about their requirements. Metamorphic testing is being used by a test engineer, who has built up a history of previous bookings. The first test case they use (T1) is as follows:
The outputs from the test ate the following accommodations:
* Villa Fianca
* Villa Palma
* Apartments Mirasol
* Villa Paris
* Apartments Miramar
Which ONE of the below options are the MOST likely outputs from the test:
Answer: C
Explanation:
The correct answer is D . Metamorphic testing derives follow-up test cases from a source test case by applying a metamorphic relation, where a controlled change in input should produce a predictable relationship in the output. The syllabus states that a metamorphic relation describes how a change in test inputs is reflected in the expected results, and that follow-up expected results are relative to the source test case rather than absolute values.
Here, the source result set contains five recommendations. If the follow-up test tightens or partitions the pool requirement, the valid recommendations should be consistent subsets of the original recommendations. Option D gives a coherent partition: T2 returns the accommodations satisfying the pool-related requirement, while T3 returns the accommodations aligned with "pool not required." It does not introduce recommendations outside the original result set and it preserves the expected relation between changed preference criteria and the returned list. Options A and B are unnecessarily narrow, while C mixes the partition by including Villa Franca in both branches.
References/topics: CT-AI Syllabus Chapter 9, Section 9.5 "Metamorphic Testing"; Section 9.5.1 "Hands- On Exercise: Metamorphic Testing."
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NEW QUESTION # 96
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