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AI+ Quality Assurance™

# AT- 920

Master AI-Driven Quality Assurance: Elevate Your Testing Efficiency, Accuracy, and Scalability

AI Testing Mastery: Gain hands-on experience with AI-powered testing tools and techniques
Intelligent Automation Edge: Streamline defect detection and performance testing using intelligent automation
QA Career Fast-Track: Accelerate your QA career with our comprehensive, industry-aligned exam bundle

213,50 €

High-Quality Video, E-book & Audiobook

Modules Quizzes

AI Mentor

Access for Tablet & Phone

Online Proctored Exam with One Free Retake

Labs Practices

Prerequisites

Programming Skills: Basic knowledge of Python and familiarity with software testing lifecycle and tools.
Basics of QA: Basic knowledge of Quality Assurance principles and practices.
Basics of AI: Foundational knowledge of machine learning concepts is beneficial but not mandatory.

Exam Details

Exam Blueprint

Modules	Percentage
Introduction to Quality Assurance (QA) and AI	7
Fundamentals of AI, ML, and Deep Learning	9
Test Automation with AI	9
AI for Defect Prediction and Prevention	9
NLP for QA	9
AI for Performance Testing	12
AI in Exploratory and Security Testing	12
Continuous Testing with AI	12
Advanced QA Technique With AI	12
Capstone Project	9

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Labs

Interactive lab sessions to apply concepts and strengthen technical skills.

Tools You'll Master

TensorFlow

SHAP (SHapley Additive exPlanations)

Amazon S3

AWS SageMaker

What Will You Learn?

QA Fundamentals

Understand the core principles of Quality Assurance (QA), including testing methodologies, tools, and processes to ensure software quality.

Manual Testing

Master manual testing techniques, including test case creation, test execution, and defect reporting to ensure software functionality meets requirements.

Automation Testing

Learn automation testing using popular tools like Selenium, Appium, and TestNG, and understand how automation enhances testing efficiency and accuracy.

Performance Testing

Gain expertise in performance testing tools like JMeter and LoadRunner, and learn how to evaluate software performance under different conditions.

Certification Modules

Module 1: Introduction to Quality Assurance (QA) and AI

1.1 Overview of QA
1.2 Introduction to AI in QA
1.3 QA Metrics and KPIs
1.4 Use of Data in QA

Module 2: Fundamentals of AI, ML, and Deep Learning

2.1 AI Fundamentals
2.2 Machine Learning Basics
2.3 Deep Learning Overview
2.4 Introduction to Large Language Models (LLMs)

Module 3: Test Automation with AI

3.1 Test Automation Basics
3.2 AI-Driven Test Case Generation
3.3 Tools for AI Test Automation
3.4 Integration into CI/CD Pipelines

Module 4: AI for Defect Prediction and Prevention

4.1 Defect Prediction Techniques
4.2 Preventive QA Practices
4.3 AI for Risk-Based Testing
4.4 Case Study: Defect Reduction with AI

Module 5: NLP for QA

5.1 Basics of NLP
5.2 NLP in QA
5.3 LLMs for QA
5.4 Case Study: Using NLP for Bug Triaging

Module 6: AI for Performance Testing

6.1 Performance Testing Basics
6.2 AI in Performance Testing
6.3 Visualization of Performance Metrics
6.4 Case Study: AI in Performance Testing of a Cloud App

Module 7: AI in Exploratory and Security Testing

7.1 Exploratory Testing with AI
7.2 AI in Security Testing
7.3 Case Study: Enhancing Security Testing with AI

Module 8: Continuous Testing with AI

8.1 Continuous Testing Overview
8.2 AI for Regression Testing
8.3 Use-Case: Risk-Based Continuous Testing

Module 9: Advanced QA Techniques with AI

9.1 AI for Predictive Analytics in QA
9.2 AI for Edge Cases
9.3 Future Trends in AI + QA

Module 10: Capstone Project

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Industry Opportunities after Certification Completion

Median Salaries

Unknown

With AI+ Quality Assurance™

Unknown

% Difference

Unknown

AI Quality Assurance Engineer

Manage AI-based automation strategies to improve testing accuracy and scalability.

NLP QA Specialist

Use NLP for bug triaging, test case generation, and team communication in QA.

Test Automation Engineer

Implement AI-driven test cases and integrate AI tools into CI/CD pipelines to streamline testing.

Defect Prediction Specialist

Apply AI and machine learning to predict and prevent defects, ensuring smoother development cycles.

Trusted Reviews by Our Learners

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Learner Success Stories

Frequently Asked Questions

Can I take the course if I’m new to quality assurance?

Yes, the course is suitable for individuals who are new to QA, as it starts with the basics and gradually builds up to more advanced concepts like AI integration into testing.

Will this course cover AI tools used in the industry?

Yes, the course covers industry-standard AI tools and platforms used for test automation, defect prediction, performance testing, and more, ensuring you stay up to date.

How will I be able to demonstrate the skills I’ve learned in this course to employers?

Upon completion, you will have a portfolio of hands-on projects, including the capstone project, which showcases your ability to apply AI in QA, making you highly competitive.

Will the course prepare me for working with cloud-based testing environments?

Yes, the course includes case studies and hands-on activities involving cloud applications, helping you leverage AI for performance and scalability testing.

What kind of real-world projects will I work on in this course?

You’ll work on projects that include defect prediction, automation of regression tests, performance testing in cloud environments, and applying AI for security testing.