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CT-AI_v1.0_World Valid Test Format & CT-AI_v1.0_World Books PDF

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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q22-Q27):

NEW QUESTION # 22
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for accuracy
B. Testing for bias
C. Testing for concept drift
D. Testing for security

Answer: C

Explanation:
Type of Testing for Stock-Price Prediction Models:Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive lossesin coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

NEW QUESTION # 23
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

A. Privacy issues
B. Security issues
C. Bias issues
D. Accuracy issues

Answer: D

NEW QUESTION # 24
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

A. Pre-labeled dataset
B. Outsourced
C. Al-Assisted
D. Internal

Answer: A

Explanation:
Labelling Approaches:Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.

NEW QUESTION # 25
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION

A. Data augmentation
B. Feature selection
C. Data sampling
D. Data labeling

Answer: C

Explanation:
* A. Feature selection
* Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
* B. Data sampling
* Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
* C. Data labeling
* Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
* D. Data augmentation
* Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training.

NEW QUESTION # 26
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

A. Using a computer vision to compare the GUI before and after the test object changes.
B. Using a pixel comparison of the GUI before and after the change to check the differences.
C. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
D. Using a vision-based detection of the GUI layout changes before and after test object changes.

Answer: B

Explanation:
A: Using a pixel comparison of the GUI before and after the change to check the differences.
* Pixel comparison is a traditional method and does not involve AI. It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
B: Using computer vision to compare the GUI before and after the test object changes.
* Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI.
C: Using vision-based detection of the GUI layout changes before and after test object changes.
* Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI.
D: Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
* An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.

NEW QUESTION # 27
......

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CT-AI_v1.0_World Valid Test Format & CT-AI_v1.0_World Books PDF

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CT-AI_v1.0_World Valid Test Format & CT-AI_v1.0_World Books PDF

Quiz ISQI - Reliable CT-AI_v1.0_World - ISTQB Certified Tester AI Testing (v1.0) Valid Test Format

ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q22-Q27):

Excellent ISQI CT-AI_v1.0_World Valid Test Format Are Leading Materials & Effective CT-AI_v1.0_World Books PDF