Grant Stone Grant Stone's Profile Page

Grant Stone Grant Stone

0 Course Enrolled • 0 Course Completed

Biography

CT-AI日本語関連対策、CT-AI資格問題集

2025年Tech4Examの最新CT-AI PDFダンプおよびCT-AI試験エンジンの無料共有：https://drive.google.com/open?id=16l53b7efvdz0y-qMiZ60C_fDZzrDryyi

ISTQBのCT-AI試験に合格するのに、私たちは最も早い時間で合格するのを追求します。私たちはお客様のための利益を求めるのを追求します。私たちはTech4Examです。Tech4ExamはISTQBのCT-AI問題集の正確性と高いカバー率を保証します。ISTQBのCT-AI問題集を購入したら、Tech4Examは一年間で無料更新サービスを提供することができます。は

ISTQB CT-AI 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

トピック 2

Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

トピック 3

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

トピック 4

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

トピック 5

Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

トピック 6

ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

トピック 7

Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

トピック 8

Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.

トピック 9

Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.

>> CT-AI日本語関連対策 <<

真実的なISTQB CT-AI日本語関連対策 & 合格スムーズCT-AI資格問題集 | 信頼できるCT-AI日本語版問題集

君はまずネットで無料な部分のISTQB認証試験をダウンロードして現場の試験の雰囲気を感じて試験に上手になりますよ。ISTQBのCT-AI認証試験に失敗したら弊社は全額で返金するのを保証いたします。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q54-Q59):

質問 # 54
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.
B. Determining if an animal is a pig or a cow based on image recognition.
C. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
D. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.

正解：A

解説：
Understanding Supervised Learning - RegressionSupervised learning is a category of machine learning where the model is trained on labeled data. Within this category,regressionis used when the goal is to predict a continuous numeric value.
* Regressiondeals with problems where the output variable is continuous in nature, meaning it can take any numerical value within a range.
* Common examples include predicting prices, estimating demand, and analyzing production trends.
* (A) Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens.#(Correct)
* This is a classicregression problembecause it involves predicting a continuous variable:daily egg productionbased on the input variablechicken's age.
* The goal is to find a numerical relationship between age and egg production, which makesregression the appropriate supervised learning method.
* (B) Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner.#(Incorrect)
* This is animage recognition task, which falls underclassification, not regression.
* Classification problems involve assigning inputs to discrete categories (e.g., "knife detected" or
"no knife detected").
* (C) Determining if an animal is a pig or a cow based on image recognition.#(Incorrect)
* This is anotherclassification problemwhere the goal is to categorize an image into one of two labels (pig or cow).
* (D) Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.#(Incorrect)
* This problem could involve a mix ofclassificationandassociation rule learning, but it does not explicitly predict a continuous variable in the way regression does.
* Regression is used when predicting a numeric output."Predicting the age of a person based on input data about their habits or predicting the future prices of stocks are examples of problems that use regression."
* Supervised learning problems are divided into classification and regression."If the output is numeric and continuous in nature, it may be regression."
* Regression is commonly used for predicting numerical trends over time."Regression models result in a numerical or continuous output value for a given input." Analysis of Answer ChoicesReferences from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles of regression-based supervised learning.

質問 # 55
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. Black box attacks based on adversarial examples create an exact duplicate model of the original.
C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
D. These examples are model specific and are not likely to cause another model trained on same task to fail.

正解：D

解説：
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 is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.

質問 # 56
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must evolve to optimize its routing
B. The robot must record the time of each delivery which is compiled into a report
C. The robot must recharge for no more than six hours a day
D. The robot must complete 99.99% of its deliveries each day

正解：A

解説：
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.

質問 # 57
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

A. Individual bias at the neuron level, and activation values of neurons in the previous layer.
B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
D. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

正解：D

解説：
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = sum (w_i cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* "Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).

質問 # 58
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

A. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.
B. The team decomposes each route into the relevant components that affect the travel time such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.
C. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.
D. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.

正解：D

解説：
Metamorphic Testing (MT)is a testing technique that verifies AI-based systems by generatingfollow-up test casesbased on existing test cases. These follow-up test cases adhere to aMetamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.
* Metamorphic testing works by transforming source test cases into follow-up test cases
* Here, thesource test caseinvolves testing themedium-speed vehicle'stravel time.
* Thefollow-up test casesare derived byextrapolating travel times for fast and slow vehiclesusing predictable relationships based on speed differences.
* MR states that modifying input should result in a predictable change in output
* Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.
* This is a direct application of metamorphic testing principles
* Inroute optimization systems, metamorphic testing often applies transformations tospeed, distance, or conditionsto verify expected outcomes.
* (B) Decomposing each route into traffic density and vehicle power#
* While useful for statistical analysis, this approach does not generate follow-up test cases based on a definedmetamorphic relation (MR).
* (C) Selecting dissimilar routes and transforming them into a fast or slow route#
* Thisdoes not follow metamorphic testing principles, which require predictable transformations.
* (D) Running fast vehicles on long routes and slow vehicles on short routes#
* This methoddoes not maintain a controlled MRand introduces too manyuncontrolled variables.
* Metamorphic testing generates follow-up test cases based on a source test case."MT is a technique aimed at generating test cases which are based on a source test case that has passed.One or more follow- up test cases are generated by changing (metamorphizing) the source test case based on a metamorphic relation (MR)."
* MT has been used for testing route optimization AI systems."In the area of AI, MT has been used for testing image recognition, search engines, route optimization and voice recognition, among others." Why Option A is Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles ofmetamorphic testing by modifying input speeds and verifying expected results.

質問 # 59
......

ISTQB試験に参加するのはあなたに自身のレベルを高めさせるだけでなく、あなたがより良く就職し輝かしい未来を持っています。Tech4ExamのCT-AI資料を利用してから、あなたは短い時間でリラクスで試験に合格することができます。我々が存在するのはあなたの成功を全力で助けるためこそです。

CT-AI資格問題集: https://www.tech4exam.com/CT-AI-pass-shiken.html

2025年Tech4Examの最新CT-AI PDFダンプおよびCT-AI試験エンジンの無料共有：https://drive.google.com/open?id=16l53b7efvdz0y-qMiZ60C_fDZzrDryyi