The course provided me with a solid understanding of IT service desk operations and troubleshooting techniques. It has improved my ability to handle user issues with confidence and professionalism.
AI+ Quality Assurance Practitioner™
AT- 920
Formerly known as AI+ Quality Assurance™ 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
- High-Quality Video, E-book & Audiobook
- Modules Quizzes
- AI Mentor
- Access for Tablet & Phone
- Online Proctored Exam with One Free Retake
- Hands-on Practices
Why This Certification Matters
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Unlock Advanced QA Skills with AI: Integrate AI and machine learning into testing to automate tasks, predict defects, and optimize performance.
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Enhance Testing Efficiency and Accuracy: Use AI tools to speed up defect detection, improve software quality, and reduce manual errors.
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Stay Ahead in a Competitive Market: Equip yourself with in-demand AI skills to meet industry standards and stand out in software testing.
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Future-Proof Your Career: Master AI technologies like NLP and defect prediction, positioning yourself for future growth in QA.
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Real-World Application and Hands-On Experience: Gain practical experience in AI techniques, preparing you to tackle complex QA challenges and improve software quality.
At a Glance: Course + Exam Overview
Program Name
AI+ Quality Assurance Practitioner™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Prerequisites
Programming Skills, Basics of QA, Foundational knowledge of machine learning concepts
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Who Should Enroll?
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QA Professionals: Looking to enhance their testing strategies with AI-driven tools and techniques.
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Software Testers: Eager to improve defect detection and automate their testing processes.
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Developers: Interested in integrating AI into the software development lifecycle for better testing efficiency.
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Data Scientists: Wanting to apply AI and machine learning principles to software quality assurance.
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Tech Managers: Seeking to stay ahead of industry trends and lead teams in AI-enhanced QA practices.
Skills You'll Gain
CI/CD IntegrationAI-Powered Predictive AnalyticsEdge AI in TestingQuantum Computing for TestingAI for Autonomous Testing
What You'll Learn
- 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
- 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
- 2.1 AI Fundamentals
- 2.2 Machine Learning Basics
- 2.3 Deep Learning Overview
- 2.4 Introduction to Large Language Models (LLMs)
- 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
- 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
- 5.1 Basics of NLP
- 5.2 NLP in QA
- 5.3 LLMs for QA
- 5.4 Case Study: Using NLP for Bug Triaging
- 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
- 7.1 Exploratory Testing with AI
- 7.2 AI in Security Testing
- 7.3 Case Study: Enhancing Security Testing with AI
- 8.1 Continuous Testing Overview
- 8.2 AI for Regression Testing
- 8.3 Use-Case: Risk-Based Continuous Testing
- 9.1 AI for Predictive Analytics in QA
- 9.2 AI for Edge Cases
- 9.3 Future Trends in AI + QA
Tools You'll Master
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
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Exam Blueprint:
- 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%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Instructor-Led (Live Virtual/Classroom)
- 5 days (live or virtual) Self-Paced: 40 hours of content
- Real-time Q&A, peer collaboration, and hands-on labs
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- 40 hours of content
- Learn anywhere, anytime, with modular quizzes to track progress
Need More Flexibility?
To help you along your way, we can help you set up a customizable payment plan. Just book a call, we’ll provide you with the details!
Let's Arrange a Call9/10 of our learners achieve their learning objectives after successful completionProcess Advisors
The course gave me a clear understanding of network security concepts and practical exposure. It helped me build confidence in handling real-world security tasks.
My path to cloud architecture had its hurdles, but Fortray was there every step of the way. They provided valuable insights into cloud technologies and architectural design. With their guidance, I turned challenges into opportunities, and today, as a Cloud Architect, I help organizations leverage the cloud for their growth and success.
The path to becoming a Cybersecurity Engineer felt like decrypting an impossible algorithm—until Fortray provided the key. Their mentorship was like a robust IPS, detecting and eliminating my weaknesses while optimizing my strengths. Now, I stand as a protector in the ever-evolving IT landscape, proud to carry their legacy forward.
This course strengthened my full-stack development skills and practical understanding. I am more confident in building and managing complete web applications.
This course improved my understanding of project management methodologies and workflows. I gained practical skills to plan and execute projects effectively.
The course provided valuable hands-on experience in data analysis tools and techniques. It enhanced my ability to extract meaningful insights from data.
The training helped me build strong analytical skills and understand how to work with real-world data science problems effectively.
The course helped me understand automation testing frameworks and best practices. I gained practical experience in writing and executing automated test cases.
Frequently Asked Questions
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.
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.
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.
Yes, the course includes case studies and hands-on activities involving cloud applications, helping you leverage AI for performance and scalability testing.
You'll work on projects that include defect prediction, automation of regression tests, performance testing in cloud environments, and applying AI for security testing.
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