P.S. Xhs1991がGoogle Driveで共有している無料かつ新しいCT-AIダンプ：https://drive.google.com/open?id=1pGEwhSfwdCwcy0KpINBYt2-Hcjj_Nbyk
ISTQB CT-AI試験を難しく感じる人に「やってもいないのに、できないと言わないこと」を言いたいです。我々Xhs1991へのISTQB CT-AI試験問題集は専業化のチームが長時間で過去のデータから分析研究された成果で、あなたを試験に迅速的に合格できるのを助けます。依然躊躇うなら、弊社の無料のISTQB CT-AIデモを参考しましょう。そうしたら、ISTQB CT-AI試験はそんなに簡単なことだと知られます。
| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Real Exam Qty: | 40 |
| Exam Duration: | 60 (75 for non-native language) |
| Exam Price: | €180 - €250 (varies by region and provider) |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Related Certifications: | ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL) |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Available Languages: | Portuguese, Spanish, Chinese, Korean, German, Japanese, French, English |
| Exam Format: | Multiple-choice questions, 1-2 points per question |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Exam Registration Pearson VUE ISTQB Official Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
短時間で試験に合格して認定資格を取得する場合は、適切なCT-AI試験問題を選択することが非常に重要です。 CT-AI学習資料にもっと注意を払う必要があります。 。すべてのお客様に適切な学習教材を提供するために、当社の多くの専門家がCT-AIトレーニング教材を設計しました。 CT-AI試験の質問を購入すると、CT-AI試験に合格して認定資格を取得するのが非常に簡単になると約束できます。
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質問 # 76
Which of the following problems would best be solved using the supervised learning category of regression?
正解：D
解説：
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."
質問 # 77
Which of the following technologies for implementing AI is considered to be a reasoning technique?
正解：C
解説：
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
質問 # 78
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：A
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.
質問 # 79
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
正解：B
解説：
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)
質問 # 80
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Choose ONE option (1 out of 4)
正解：A
解説：
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non- fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor.
Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.
TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:
* Precision protects legitimate customers by minimizing false alarms.
* Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.
質問 # 81
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