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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Exam Format: | Multiple Choice |
| Available Languages: | English |
| Exam Price: | EUR 250 |
| Exam Duration: | 60 minutes |
| Related Certifications: | ISTQB CTAL-TTA ISTQB CTFL |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Lifetime (no expiration) |
| Passing Score: | 65% |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
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NEW QUESTION # 129
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?
Answer: D
Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision- making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
NEW QUESTION # 130
Which ONE of the below is MOST likely to be a challenge in ML data preparation?
Answer: B
Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 131
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Answer: B
Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretability that interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must consider user background, and explanations must be comprehensible to the intended user group.
NEW QUESTION # 132
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION
Answer: B
Explanation:
A . Regression
Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
B . Association
Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
C . Clustering
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
D . Classification
Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.
NEW QUESTION # 133
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
Answer: B
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
Clarifying the Other Options:
A). A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B). A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D). A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).
NEW QUESTION # 134
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