AIAP® Technical Assessment Past Years Series

AI Singapore’s AI Apprenticeship Programme (AIAP)® was launched in 2018 to groom local Singaporean AI talent and enhance their career with AI-related skills. Since its launch, there have been 14 batches of apprentices who have embarked on this award-winning programme.

Before embarking on the AIAP journey, all apprentices have to pass our recruitment process, which includes a Technical Assessment stage. The Technical Assessment is a 6-day take-home assignment, and it requires candidates to perform exploratory data analysis (EDA) and build an end-to-end machine learning pipeline on an unseen problem statement and dataset.

To ensure the rigor of the selection process, all Technical Assessments are carefully designed by our AI Engineers to effectively assess applicants’ proficiency in AI and Software Engineering. Based on past statistics, only 25% of the total applicants will pass the Technical Assessment and proceed to the second stage of the recruitment process (Interview).

To assist aspiring AIAP apprentices in embarking on their AIAP journey, we have set up the AIAP Technical Assessment Past Years Series repository, a public repository that contains our past AIAP Technical Assessments. We know that these resources will be valuable for aspiring AIAP apprentices when they are preparing for the AIAP Technical Assessments, and for those who simply want to have an avenue to hone their AI proficiency.

For a start, we will release the Batch 9 Technical Assessment, which requires candidates to build models that can predict the students’ O-level mathematics examination scores. It is a comprehensive and challenging assessment, and we believe that it will be a rewarding experience when attempting the assessment.

We will regularly update the AIAP Technical Assessment Past Years Series to provide new assessments for aspiring AIAP apprentices to develop their AI proficiencies. We also highly recommend candidates to refer to our AIAP Field Guide as these two learning resources strongly complement one another and can accelerate your learning in the field of AI.