仕事や学業の合間にISQI ISTQB Certified Tester AI Testing (v1.0)の対策を進めるのは、想像以上に大変です。CertShikenのCT-AI_v1.0_World練習問題は40問に厳選されているため、限られた時間でも効率よく弱点を潰していけます。

ISQI CT-AI_v1.0_World 試験概要:

認定ベンダー:	iSQI / ISTQB
試験名:	ISTQB Certified Tester AI Testing (v1.0)
試験番号:	CT-AI_v1.0
対応言語:	English
試験時間:	60 分
認定の有効期間:	有効期限なし (無期限)
試験形式:	選択式問題
関連資格:	ISTQB Certified Tester Foundation Level (CTFL)
サンプル問題:
受験方法:	認定プロバイダー（iSQIなど）を通じたオンラインまたはオンサイトでの監督付き試験
前提条件:	ISTQB Foundation Level認定を推奨

ISQI CT-AI_v1.0_World 試験シラバストピック:

CT-AI_v1.0_World試験の疑問を解決するFAQ

ISQI ISTQB Certified Tester AI Testing (v1.0) 認定 CT-AI_v1.0_World 試験問題：

問題 #1

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

問題 #2

Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

A. Challenges resulting from low accuracy of the models.
B. The challenge of mimicking undefined scenarios generated due to self-learning
C. The challenge of providing explainability to the decisions made by the system.
D. Challenges in the creation of scenarios of human handover for autonomous systems.

問題 #3

"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

A. A lack of focus on non-functional requirements testing.
B. The input data has not been tested for quality prior to use for testing.
C. A lack of focus on choosing the right functional-performance metrics.
D. A lack of similarity between the training and testing data.

問題 #4

A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
B. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
C. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

解説：