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AI+ Quality Assurance Practitioner (AT-920)

Software testing becomes harder as applications grow, release cycles shorten, and teams manage more complex systems. AI offers practical ways to address these pressures.

The AI+ Quality Assurance Practitioner certification focuses on using AI to improve testing speed, accuracy, and efficiency. It covers automation, defect prediction, performance testing, security testing, and continuous testing. Practical exercises and case studies help connect these skills with everyday QA work.

Overview

Overview

AI can help quality assurance teams reduce repetitive work, find defects earlier, and improve testing decisions. This course explains how AI supports test planning, automation, defect prediction, performance testing, and software security.

Participants also explore machine learning, deep learning, and language-based AI within practical QA tasks. The course covers continuous testing, risk-based testing, and testing within software delivery pipelines. Hands-on activities build practical experience, while the final project brings these skills together in an AI-supported QA solution.

Prerequisites
  • Basic understanding of Python for hands-on exercises and certification tasks

  • Familiarity with QA processes, including test planning, execution, reporting, and common testing tools

  • Basic understanding of AI and machine learning concepts for applying AI within QA practices

Target Audience
  • QA professionals expanding their testing skills with AI

  • AI enthusiasts exploring practical AI applications in quality assurance

  • Software developers improving testing workflows with AI

  • Data analysts applying AI to testing and data-driven decisions

  • Project managers leading AI-supported QA teams

  • Technology innovators exploring advanced AI-driven testing techniques

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 Techniques with AI – 12%
  • Capstone Project – 9%
FAQs

1. Will I learn how to use AI within continuous testing?
Yes. The course covers AI-supported regression testing, continuous testing, risk-based testing, and integration with software delivery pipelines.

2. Can the course be taken online?
Yes. The certification is available through live virtual instructor-led training or a self-paced online learning option.

3. Is in-person training available?
Yes. Classroom instructor-led training is available through AI CERTs Authorized Training Partners.

Course Outline

Module 1: Introduction to Quality Assurance (QA) and AI
  1. Overview of quality assurance
  2. Core QA processes and methodologies
  3. Introduction to AI in QA
  4. AI-supported defect detection
  5. Predictive analytics in testing
  6. QA metrics and KPIs
  7. Measuring QA process performance
  8. Aligning QA measures with business goals
  9. Role of data in QA
  10. AI-driven data strategies
  11. Improving testing efficiency and accuracy

 

Module 2: Fundamentals of AI, ML, and Deep Learning
  1. Fundamentals of artificial intelligence
  2. Machine learning basics
  3. Supervised learning
  4. Unsupervised learning
  5. Reinforcement learning
  6. Machine learning applications in QA
  7. Deep learning fundamentals
  8. Neural networks
  9. Image and log analysis
  10. Anomaly detection
  11. Large Language Models
  12. GPT and BERT
  13. Language-based AI applications in QA

 

Module 3: Test Automation with AI
  1. Test automation fundamentals
  2. Automation principles and methodologies
  3. AI-driven test case generation
  4. Creating tests for changing software requirements
  5. AI-enabled testing tools
  6. Streamlining testing workflows
  7. Integration with CI/CD pipelines
  8. Continuous software testing
  9. Reducing human error
  10. Improving testing speed and reliability
  11. Supporting Agile and DevOps workflows

 

Module 4: AI for Defect Prediction and Prevention
  1. Defect prediction techniques
  2. Historical and real-time data analysis
  3. Predicting system vulnerabilities
  4. Preventive QA practices
  5. Identifying issues earlier
  6. Machine learning for defect prediction
  7. Predictive analytics
  8. Risk-based testing
  9. Prioritizing high-risk areas
  10. Optimizing testing resources
  11. Continuous AI monitoring
  12. Real-time quality oversight
  13. Improving software reliability

 

Module 5: NLP for QA
  1. Natural Language Processing fundamentals
  2. Tokenization
  3. Syntax parsing
  4. Semantic analysis
  5. NLP applications in QA
  6. Structured and unstructured data analysis
  7. Extracting useful information from testing data
  8. Large Language Models for QA
  9. GPT and BERT applications
  10. Understanding complex queries
  11. Automated responses
  12. NLP for bug resolution
  13. Software issue analysis
  14. Bug identification and categorization

 

Module 6: AI for Performance Testing
  1. Performance testing fundamentals
  2. System scalability and reliability
  3. AI-supported performance testing
  4. Automated performance analysis
  5. Identifying performance bottlenecks
  6. Optimizing resource use
  7. Visualizing performance metrics
  8. AI-driven graphs and dashboards
  9. Turning performance data into useful insights
  10. Predicting system behavior
  11. Testing under changing workloads
  12. Improving overall system performance

 

Module 7: AI in Exploratory and Security Testing
  1. Exploratory testing with AI
  2. Automated exploration of software behavior
  3. Identifying edge cases
  4. Detecting unexpected interactions
  5. AI in security testing
  6. Vulnerability scanning
  7. Anomaly detection
  8. Threat prediction
  9. Advanced security testing methods
  10. Automated penetration testing
  11. Adversarial simulations
  12. AI for threat analysis
  13. Emerging threat detection
  14. Real-time security insights
  15. Proactive software defense

 

Module 8: Continuous Testing with AI
  1. Continuous testing fundamentals
  2. Continuous testing in Agile environments
  3. Continuous testing in DevOps
  4. Faster testing feedback
  5. AI-supported regression testing
  6. Machine learning for test execution
  7. Reducing regression risks
  8. Predictive testing approaches
  9. Test optimization
  10. Risk-based continuous testing
  11. Prioritizing tests by potential risk
  12. Identifying defects earlier
  13. Improving release cycles
  14. Supporting reliable software delivery

 

Module 9: Advanced QA Techniques with AI
  1. Predictive analytics in QA
  2. Historical testing data analysis
  3. Predicting defects
  4. Predicting performance bottlenecks
  5. Identifying testing risks
  6. Prioritizing testing efforts
  7. AI for edge cases
  8. Identifying rare testing scenarios
  9. Handling complex testing conditions
  10. Future trends in AI and QA
  11. Emerging AI technologies
  12. AI integration with connected technologies
  13. Advanced AI-supported QA environments

 

Module 10: Capstone Project
  1. Applying knowledge from previous modules
  2. Designing an AI-driven QA solution
  3. Applying AI within a practical QA scenario
  4. Using test automation concepts
  5. Applying defect prediction methods
  6. Using language-based AI within QA tasks
  7. Combining AI techniques across testing workflows
  8. Addressing practical QA challenges
  9. Building a complete AI-supported QA solution
Note : A representative from Datacipher will contact you with further details
Payment Methods

At DataCipher, we offer a variety of payment options for our Fortinet courses. Here are the methods available:

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These options are designed to make the registration process as smooth and flexible as possible for all participants.

Status

Guaranteed to Run – DataCipher is committed to running this class unless unforeseen events such as an instructor’s accident or illness occur.

Guaranteed on Next Booking – The course will proceed once an additional student registers.

Scheduled Class – We have scheduled this course and rarely cancel due to low enrollment. We offer a “Cancel No More Than Once” guarantee, ensuring that if a class is canceled due to insufficient enrollment, the next session will run regardless of the number of attendees.

Sold Out – If the class is fully booked, please use our contact form to join the waiting list or to inquire about additional sessions. We’re here to accommodate your training needs and keep you informed of new opportunities.

Half and Full-Day Training

At DataCipher, we offer our training courses in both traditional full-day and convenient half-day formats. Our half-day classes are specifically designed for IT professionals who cannot be away from their workplaces for consecutive full days. This flexible schedule allows participants to dedicate a few hours to learning and then return to their regular work responsibilities.

The curriculum for both the full-day and half-day formats is identical. The primary difference is that the half-day classes spread the coursework over a more extended period, providing a balanced approach to professional education. DataCipher has been successfully running these half-day training sessions for several years, receiving consistently positive feedback from our customers. They appreciate the flexibility and report that the extended timeframe facilitates a deeper understanding of the material, as it gives them more time to absorb and reflect on the information learned.

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