AI Testing Certification Quiz
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Questions and Answers

Which group is NOT a primary target audience for the AI Testing syllabus?

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Which of the following best describes the purpose of the learning objectives regarding the syllabus?

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What is primarily specified by the 'Certified Tester AI Testing Overview' document?

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A tester encounters the term 'model drift'. According to the syllabus, what is the minimum cognitive level expected of this?

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For which role listed is the AI Testing certification LEAST directly relevant?

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A training provider is planning an AI testing course using this syllabus. What is ONE primary purpose of the syllabus for them?

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Which cognitive level would best describe a student being asked to elaborate on the benefits of using different testing techniques?

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When reading AI Testing syllabus materials, at what level should a candidate recognize a keyword like 'adversarial attack'?

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What is a typical component of a contract for AIaaS?

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What is a common practice when an AIaaS provider doesn't meet the contracted availability?

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What kind of liability is typically offered in most AIaaS contracts?

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What is the main purpose of the initial free trial period that's often included with AIaaS?

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How is the pricing structured for IBM Watson Assistant?

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How is pricing generally determined for Google Cloud AI document processing services?

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Which of the following is NOT a principle for the responsible stewardship of trustworthy AI?

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What primary function does Amazon CodeGuru provide?

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What is a key characteristic of 'reward hacking' in AI systems?

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How are the prices determined for Microsoft Azure Cognitive Search?

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Which of these best describes a negative side effect in AI systems?

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Why is transparency important for AI systems?

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What should organizations and individuals do when developing AI systems?

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What does the concept of 'robust, secure and safe' AI systems imply?

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Which scenario describes a possible 'side effect' of an AI system?

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What is the primary focus of ‘value-based principles’ in AI stewardship?

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What is a primary advantage of using pre-trained models?

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For which types of technologies are pre-trained models commonly available?

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What does the term 'transfer learning' refer to?

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According to the content, which part of a deep neural network typically performs more basic tasks?

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How does the similarity between the pre-trained model's original function and the new required task affect the effectiveness of transfer learning?

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Which scenario best demonstrates the effective use of transfer learning according to the content?

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What is the possible method for using a pre-trained model without modification?

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What is a key benefit of using resources such as the ImageNet dataset according to the text?

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What is the primary factor determining the type of data used by an algorithm and model?

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In the context of training a machine learning model, what does an 'epoch' refer to?

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Which of the following best describes the purpose of a validation dataset in model development?

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What is a model hyperparameter?

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What is a key consideration when selecting data for training, tuning, and testing a machine learning model?

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What is the primary purpose of evaluating a machine learning model?

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What is the typical approach when choosing a model for an intended application?

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What is the main goal of the tuning phase of a model during its development process?

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Which of the following is NOT a typical challenge associated with data preparation?

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What is the primary purpose of data visualization in the context of data analysis?

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During the data preparation process, some steps are performed once and some may be automated. Which of the following activity would likely be automated?

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In the context of Machine Learning (ML) model development, what is the main characteristic of a training dataset?

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Why is checking for defects during data preparation a critical activity?

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When creating a machine learning model, how should data be split to develop a model effectively?

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Which of the following best describes 'sample bias' in the context of data preparation?

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What is the next step, in the provided process, after data preparation for an ML model?

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Study Notes

Certified Tester Al Testing (CT-AI) Syllabus

  • This syllabus is for the ISTQB® Certified Tester Al Testing.
  • The ISTQB® offers this syllabus to member boards for translation into local languages and accrediting training providers.
  • Member boards can adapt the syllabus to local language needs and adjust references.
  • Certification bodies can use this syllabus to create examination questions.
  • Training providers use the syllabus to develop courseware and teaching methods.
  • Certification candidates use the syllabus to prepare for the certification exam.
  • The international software and systems engineering community may use the syllabus as a basis for books and articles.

Revision History

  • Version 1.0 was released on 2021/10/01 for general availability (GA).

Table of Contents

  • Includes sections on copyright notice, revision history, table of contents, acknowledgements, introduction, and more.
  • Covers introduction to Al (105 minutes), Quality Characteristics for Al-Based Systems (105 minutes), Machine Learning (145 minutes), ML-Data (230 minutes), ML Functional Performance Metrics (120 minutes), ML - Neural Networks and Testing (65 minutes), Testing Al-Based Systems Overview (115 minutes), Testing Al-Specific Quality Characteristics (150 minutes), Methods and Techniques for the Testing of Al-Based Systems (245 minutes), Test Environments for Al-Based Systems (30 minutes), Using Al for Testing (195 Minutes), Appendix A – Abbreviations, Appendix B – Al Specific and other Terms and an Index.

Introduction

  • Purpose of this syllabus: To provide a foundation for the ISTQB® Certified Tester Al Testing.
  • Target audience: Testers, test analysts, data analysts, test engineers, consultants, test managers, user acceptance testers, software developers, project managers, quality managers, software development managers, business analysts, operations team members, and IT directors.
  • Examinable learning objectives: Support business outcomes; used for creating exams in the ISTQB® Certified Tester Al Testing.
  • Cognitive learning objectives: Classify (k1 and k2) and apply (K3 and K4) knowledge.
  • Hands-on objectives: Covers practical skills and competency, with specific exercise guidelines.

Accreditation

  • ISTQB® Member Boards can accredit training providers for use of their course materials based on this syllabus.
  • Training providers will need to follow accreditation guidelines provided by the relevant member board or organization to gain accreditation.

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Related Documents

ISTQB CT-AI Syllabus v1.0 PDF

Description

Test your knowledge on the AI Testing syllabus with this quiz. It covers key concepts, cognitive levels, and certification relevance for professionals in AI testing. Perfect for anyone looking to understand the framework and objectives outlined in the syllabus.

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