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CertShikenが提供するCT-AI資料は比べものにならない資料です。これは前例のない真実かつ正確なものです。CT-AI受験生のあなたが首尾よくCT-AI試験に合格することを助けるように、当社のISTQBエリートの団体はずっと探っています。CertShikenが提供した製品は真実なもので、しかも価格は非常に合理的です。CertShikenの製品を選んだら、あなたがもっと充分の時間でCT-AI試験に準備できるように、当社は一年間の無料更新サービスを提供します。そうしたら、試験からの緊張感を解消することができ、あなたは最大のメリットを取得できます。
質問 # 110
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?
正解：B
解説：
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)
質問 # 111
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION
正解：B
解説：
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.
質問 # 112
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
正解：B
解説：
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing."
質問 # 113
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
正解：C
解説：
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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)
質問 # 114
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)
正解：D
解説：
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches
. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous 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 .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregression problem. Option D is incorrect because classification can involveany number of classes, not only two.
Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.
質問 # 115
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