AI+ Quality Assurance™ - eLearning (exam included)

AI+ Quality Assurance™ - eLearning (exam included)

The AI+ Quality Assurance certification equips you with the skills and knowledge to incorporate AI into QA practices, boosting innovation and testing efficiency. Throughout the program, you will explore how AI transforms traditional QA processes, including test planning, execution, defect prediction, and performance testing. You’ll build a solid foundation in AI, machine learning, deep learning, and natural language processing, learning to apply these technologies across various QA scenarios. Hands-on exercises and real-world case studies will help you develop practical skills in automating test cases, predicting defects, and leveraging AI-powered QA techniques.

Participants will also engage in exercises demonstrating how AI can optimize QA workflows, improve decision-making, and enhance overall testing efficiency. The certification includes a capstone project where you will design and implement an AI-driven QA solution, applying the knowledge gained throughout the course. By completion, you will be prepared to integrate AI into QA processes, increasing both testing speed and accuracy while boosting organizational performance.

AI-Powered Quality Assurance: 

• Enhance testing efficiency, accuracy, and scalability using AI-driven methods.
• Hands-On Practice: Gain practical experience with advanced AI testing tools and techniques.
• Intelligent Automation: Optimize defect detection and performance testing through smart automation.
• Career Advancement: Boost your QA expertise with a complete, industry-focused exam preparation bundle.

Why This Certification Matters

• AI-Powered Project Forecasting: Use AI to predict project risks and adjust

• Advance QA Skills with AI: Leverage AI and machine learning to automate testing, predict defects, and enhance performance.

• Boost Efficiency and Accuracy: Accelerate defect detection, improve software quality, and reduce manual errors using AI tools.
• Stay Competitive: Gain in-demand AI skills to meet industry standards and stand out in software testing.
• Future-Proof Your Career: Master technologies like NLP and defect prediction for long-term growth in QA.
• Hands-On, Real-World Experience: Apply AI techniques to practical scenarios, improving your ability to handle complex QA challenges.
  
  

Industry Growth: Driving Data-Driven Innovation Across Sectors

• Market Growth: The global AI-enabled testing market is projected to grow from USD 856.7 million in 2024 to USD 3,824.0 million by 2032, at a CAGR of 20.9% (Source: Fortune Business Insights).
• Continuous Delivery: Adoption of continuous delivery is driving AI-driven testing for faster, higher-quality software releases.
• AI-Powered Testing: Defect prediction and risk-based testing are becoming standard, improving accuracy and reducing manual effort.
• Automation Demand: Advancing AI technologies are increasing the need for AI-based test automation, enhancing software delivery speed and quality.
• Organizational Investment: Companies are heavily investing in AI-driven QA tools to innovate, cut costs, and ensure superior software performance.

Key Features

• Course and material in English 
• Intermediate level  (Category: AI+ Technical)
• 1 year access to the platform 24/7
• 40 hours of video lessons & multimedia resources

• 50 hours of study time recommendation 
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake

• Certification of completion included valid for 1 year

• Virtual Hands-on Lab included
• Tools You’ll Master: TensorFlow, SHAP, Amazon S3, AWS SageMaker

Learning Outcomes

• QA Fundamentals: Learn essential QA principles, testing methodologies, tools, and processes to maintain software quality.
• Manual Testing: Develop skills in creating and executing test cases, and reporting defects to verify software meets requirements.
• Automation Testing: Gain proficiency with automation tools such as Selenium, Appium, and TestNG, improving testing speed and accuracy.
• Performance Testing: Master tools like JMeter and LoadRunner to assess software performance under various conditions.

Target Audience

• QA Professionals: Aiming to upgrade testing strategies using AI-powered tools and methods.
• Software Testers: Looking to enhance defect detection and automate testing workflows.
• Developers: Interested in incorporating AI into the software development process for improved testing efficiency.
• Data Scientists: Wanting to apply AI and machine learning techniques to software quality assurance.
• Tech Managers: Seeking to stay current with industry trends and lead teams in AI-driven QA practices.

Prerequisites

• Programming Skills: Basic Python knowledge and some experience with software testing.
• Quality Assurance Fundamentals: Understanding of core QA principles and practices.
• AI Basics: Familiarity with machine learning concepts is helpful but not required.

Exam Details

• Duration: 90 minutes
• Passing :70% (35/50)
• Format: 50 multiple-choice/multiple-response questions
• Delivery Method: Online via proctored exam platform (flexible scheduling)
• Language: English

Course Content

Module 1: Introduction to Quality Assurance and AI

1.1 Introduction to Quality Assurance (QA) and AI 

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

Licensing and accreditation

This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements. 

Equity Policy

AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.

FAQ

Is this course suitable for beginners in QA?
Yes, it’s designed for newcomers, starting with foundational QA concepts and gradually advancing to AI-enhanced testing techniques.

Does the course cover AI tools used in the industry?
Yes, it includes hands-on training with industry-standard AI tools for test automation, defect prediction, performance testing, and more.

How can I showcase my skills to employers?
You’ll build a portfolio of practical projects, including a capstone, demonstrating your ability to apply AI in QA.

Will the course prepare me for cloud-based testing?
Yes, it provides case studies and exercises using cloud applications, enabling AI-driven performance and scalability testing.

What real-world projects will I work on?
Projects include defect prediction, regression test automation, cloud performance testing, and AI applications for security testing.

Recertification Requirements
AI+ Technical courses require recertification every year to keep your certification valid. Notifications will be sent three months before the due date

How Can AVC Help Foster an AI-Ready Culture?
While AI offers significant advantages, many organizations struggle with challenges like talent gaps, complex data environments, and system integration barriers. At AVC, we understand these obstacles and have tailored our certification programs to help businesses overcome them effectively.

Our strategic approach focuses on building a culture that embraces AI adoption and innovation. Through our industry-recognized certifications and in-depth training, we equip your workforce with the skills and knowledge needed to lead your organization confidently into an AI-powered future.

Customized for Impact: Our programs aren't one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles.

Practical, Real-World Learning: We prioritize hands-on experience over theory, using real-world projects and case studies. This approach ensures your team gains the confidence and capability to implement AI solutions effectively, driving innovation and measurable business outcomes.