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NEW QUESTION # 142
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data. In this case, it is decided it is more important that emergency services are not sent un-necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?
Answer: A
Explanation:
The correct answer is D. Precision . In this scenario, a positive prediction means that the system detects a possible heart issue and dispatches emergency services. The stated priority is to avoid sending emergency services unnecessarily. That means the test engineer must minimize false positives , because a false positive would incorrectly classify a patient as needing emergency assistance. The CT-AI syllabus states that precision is suitable when the cost of false positives is high and confidence in positive outcomes needs to be high.
Accuracy is a weak choice because the data is imbalanced: most patients are not expected to have heart issues, so a model could appear accurate while still making unacceptable emergency-dispatch errors. MSE and R- squared are regression metrics, not classification metrics, and the problem is a classification decision: dispatch or do not dispatch. Recall would be appropriate if the main concern were not missing true heart issues, but the question explicitly prioritizes avoiding unnecessary dispatches. Therefore, precision is the best functional performance metric for this acceptance objective.
References/topics: CT-AI Syllabus Chapter 5, Sections 5.1 and 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 143
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?
Answer: D
Explanation:
The correct answer is B . In unsupervised learning, the ML model is created from unlabeled data . The algorithm infers patterns in the input data and assigns inputs to groups or classes based on commonalities, rather than learning from known output labels. The CT-AI syllabus identifies clustering and association as the main unsupervised learning problem types.
Missing information about whether previous loans were granted and repaid is essentially missing outcome label information. That would be highly relevant for supervised learning, where the model learns from input data and corresponding labels, but it is least directly relevant to an unsupervised approach. By contrast, options A, C, and D are classic dataset quality issues. Missing account records indicate incomplete data; inconsistent pre-processing can create data-format and comparability defects; and irrelevant account information may adversely influence results or waste modelling resources. The syllabus explicitly lists incomplete data, data not pre-processed, and irrelevant data as typical dataset quality issues affecting ML models.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.2 "Unsupervised Learning"; Chapter 4, Section 4.3 "Dataset Quality Issues"; Chapter 4, Section 4.4 "Data Quality and its Effect on the ML Model."
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NEW QUESTION # 144
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
Answer: A
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
Clarifying the Other Options:
A). A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B). A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D). A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).
NEW QUESTION # 145
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
Answer: D
Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately."
NEW QUESTION # 146
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
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.
:
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 # 147
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