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NEW QUESTION # 131
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
Answer: B
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
:
ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
NEW QUESTION # 132
Which of the following is correct regarding the layers of a deep neural network?
Answer: A
Explanation:
A deep neural network (DNN) is a type of artificial neural network that consists of multiple layers between the input and output layers. The ISTQB Certified Tester AI Testing (CT-AI) Syllabus outlines the following characteristics of a DNN:
Structure of a Deep Neural Network:
A DNN comprises at least three types of layers:
Input layer: Receives the input data.
Hidden layers: Perform complex feature extraction and transformations.
Output layer: Produces the final prediction or classification.
NEW QUESTION # 133
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?
Answer: B
Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.
NEW QUESTION # 134
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age.
When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age.
Which ONE of the following approaches do you expect to be MOST likely to succeed?
Answer: C
Explanation:
A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age verification.
NEW QUESTION # 135
Which of the following are the three activities in the data acquisition activities for data preparation?
Answer: D
Explanation:
According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, data acquisition, a critical step in data preparation for machine learning (ML) workflows, consists of three key activities:
* Identification:This step involves determining the types of data required for training and prediction. For example, in a self-driving car application, data types such as radar, video, laser imaging, and LiDAR (Light Detection and Ranging) data may be identified as necessary sources.
* Gathering:After identifying the required data types, the sources from which the data will be collected are determined, along with the appropriate collection methods. An example could be gathering financial data from the International Monetary Fund (IMF) and integrating it into an AI-based system.
* Labeling:This process involves annotating or tagging the collected data to make it meaningful for supervised learning models. Labeling is an essential activity that helps machine learning algorithms differentiate between categories and make accurate predictions.
These activities ensure that the data is suitable for training and testing machine learning models, forming the foundation of data preparation.
NEW QUESTION # 136
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