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AI Engineer for Data Scientists Associate

Updated

Certification Requirements

DataCamp's AI Engineer for Data Scientists Associate Certification is awarded to individuals who successfully complete two timed exams and one practical exam (AIEDSA101, AIEDSA102 & AIEDSA501P). To be successful, you will be required to demonstrate competency in the following six domains:

  • Data Management
  • Exploratory Analysis
  • Programming for AI Data Science 
  • Governance for AI
  • Model Development for AI
  • Production System and Application Development for AI

The AIEDSA101 is a 2-hour exam that assesses data management, model development, and exploratory analysis. To successfully pass this exam, you should be able to:

  • Assess data quality and perform validation tasks.
  • Prepare data for modeling by implementing relevant transformations.
  • Assess and define modeling approaches for deep learning and other machine learning problems.
  • Perform standard data import, joining and aggregation tasks using Python.
  • Implement standard modeling approaches for deep learning and other machine learning problems.
  • Use suitable methods to assess the performance of a model.
  • Calculate metrics to effectively report characteristics of data and relationships between features using Python.
  • Use data visualization tools to demonstrate the characteristics of data.
  • Read and analyze data visualizations to represent the relationships between features.

The AIEDSA102 is a 2-hour exam that assesses governance, programming for data science, and production system and application development. To successfully pass this exam, you should be able to:

  • Explain ethics and bias concerns in relation to generative AI
  • Identify key privacy laws and themes surrounding data use, such as GDPR, CCPA and PIPL, and the themes of data sovereignty, data breach notifications, user data requests, increased transparency, accountability, and penalties for violating privacy law.
  • Use common programming constructs to write repeatable production-quality code for analysis in Python.
  • Describe software development and deployment best practices and workflows and explain their benefits for AI systems.
  • Demonstrate awareness of basic concepts in MLOps and LLMOps.
  • Prepare data for use in ETL and ELT pipelines.
  • Create prototype systems and applications that utilize generative AI.

What to expect on the practical exam

The final step in this certification is the AIEDSA501P practical exam. The practical exam will test knowledge skills and abilities across all domains and must be completed in Python.

The practical exam will combine multiple competencies tested in the timed exam. No new domains or competencies will be tested in the practical exam. 

You will be given 5 test items related to a real-world scenario. Individual items may require skills from multiple competencies. 

You may be tested on any of the competencies that are included within this certification.

How long do I have to complete the practical exam?

You will have a total of 4 hours to complete your practical exam. You’ll have 30 days from the time you register for certification to the time you must complete all requirements, including the exam(s) and practical exam. If you complete the exam(s) right away, then you’ll have the remaining time to work on preparing for the practical exam. However, if you take longer to complete the exam(s), you’ll have less time to complete the practical exam.

Re-taking your certification

Candidates who are unsuccessful in any component will have to wait 14 days before they can attempt the certification again. Upon retaking the certification, you will be required to complete all exams again, including any that you may have passed on a previous attempt.

If you have questions or feedback related to certification, please submit your inquiry here.