Examination regulations — Amsterdam Data Academy

Examination and Assessment Regulations

Amsterdam Data Academy Version 3.1, September 2026 Approved by: Amsterdam Data Academy Management & Examination Committee

1. Purpose

These Examination and Assessment Regulations describe how assessments, examinations, exemptions and progression decisions are organised at Amsterdam Data Academy. They ensure transparency, fairness, and alignment with the published learning outcomes as described in the programme study guide.

These regulations apply to all learners enrolled in courses and bootcamps offered by Amsterdam Data Academy, including the B11 Applied AI & Datascience full programme and the B14 Cybersecurity en Digitale Soevereiniteit in het AI-tijdperk programme.

2. Scope

These regulations apply to all programmes, including but not limited to:

Code	Programme	Type
B11	Applied AI & Datascience	Full programme
B14	Cybersecurity en Digitale Soevereiniteit in het AI-tijdperk	Full programme
B01	Datascience & Python	Bootcamp
B02	Data & Analytics	Bootcamp
B03	Data Professional (Analytics & Datascience)	Bootcamp
B04	Data Engineering	Bootcamp
B05	AI Engineering	Bootcamp
B06	Data Expert (Datascience & Data Engineering)	Bootcamp
B07	Applied AI for Business Professionals	Bootcamp
B08	AI Professional (Applied AI & AI Engineering)	Bootcamp

Programme-specific details such as learning outcomes, duration and assessment formats are described in the relevant study guides.

3. Assessment Principles

Assessments at Amsterdam Data Academy are designed according to the following principles:

All assessments are aligned with published learning outcomes
Assessment methods are valid, reliable and transparent
Learners are assessed on knowledge, skills and professional application
Assessment criteria and rubrics are communicated in advance via the learning platform
Assessments are designed to support learning as well as evaluation

4. Forms of Assessment

Depending on the programme, assessment may include one or more of the following:

Knowledge tests: closed questions, taken digitally in the learning environment
Practical assignments: real-world business cases or technical projects (e.g. Jupyter notebooks, dashboards, architecture reports, or tasks in the learner’s own work environment)
Case studies: applied analysis of a concrete business scenario
Practical tests: supervised execution of tasks in a practice lab
Presentations: live or recorded delivery (max 10 slides or 3-minute demo)
Final assignments: integrated capstone projects per bootcamp track or programme
Internship report & presentation: including written report, stakeholder presentation and supervisor evaluation

The assessment structure and weighting per programme are described in the study guide and assessment matrix.

5. Grading and Passing Criteria (Cesuur)

Every module is assessed using a 4-point rubric scale across weighted criteria, producing a score out of 300 points. Knowledge tests are scored as percentage correct.

Score	Label	Percentage	Result
3 · Good	Pass with Distinction	≥ 85% (≥ 255/300)	Excellent
2 · Sufficient	Pass	55–84% (165–254)	Diploma eligible
1 · Needs Work	Resubmission	35–54% (105–164)	One resit allowed
0 · Insufficient	Fail	< 35% (< 105)	Module resit required

Passing threshold (cesuur): A score of ≥ 165 out of 300 points (55%) is required to pass each module assessment. For knowledge tests the threshold is 55% correct. A score of ≥ 210/300 (70%) qualifies as Good; ≥ 255/300 (85%) qualifies as Excellent.

The detailed assessment rubrics per bootcamp, including all criteria, weights, and descriptions per score level, are published in a separate document: Assessment rubrics (PDF). The rubrics for B14 are part of the B14 study guide.

Diploma requirements (B11 programme):

Achieve Sufficient (≥ 165/300) on all module assessments
Achieve Sufficient on all five Final Assignments (A51–A55)
Complete the internship with minimum Sufficient on all criteria
Attend minimum 80% of live sessions
Submit all portfolio items by stated deadlines

Diploma with Distinction (Cum Laude): Learners achieving Good on the internship AND at least 3 track Final Assignments receive the diploma cum laude.

Diploma requirements (B14 programme):

Achieve Sufficient on all nine modules
Achieve Sufficient on the Final Assignment (A56), carried out on a real workplace
Attend minimum 80% of live sessions
Submit all assignments by stated deadlines

6. Four-Eyes Principle

Amsterdam Data Academy applies a mandatory four-eyes principle (vier-ogenbeginsel) for all assessments. Every module has a designated teacher (begeleider) and an independent examiner (examinator). The teacher guides learning; the examiner assesses independently. This two-person principle is structurally embedded in our curriculum management system and applies to every module and final assignment.

The Examination Committee oversees the examination process and ensures quality and consistency across all assessments.

7. Resits and Retakes

Learners scoring 1 (Needs Improvement) receive one resit opportunity per module. Resit assignments are new versions of the original task at equivalent difficulty. A resit takes place within four weeks and covers only the insufficient components. Within B14, a maximum of 70% of the achievable score can be obtained after a resit.
The examiner for the resit is independent of the original assessment.
Learners scoring 0 (Insufficient) on a Final Assignment must repeat the full track.
For the internship, a structured improvement assignment is offered after an insufficient evaluation.

8. Final Projects and Practical Assignments

Each bootcamp track concludes with a Final Assignment, an integrated case project demonstrating mastery. The B11 programme uses the Vivino case as a consistent real-world business context across all five tracks, progressing from analytics dashboards (Track 1) to a production-grade RAG AI sommelier (Track 5).

Final projects are assessed using a detailed rubric and reviewed by two assessors (four-eyes principle).

The B14 programme concludes with a sovereignty scan of the learner’s own workplace, assessed with a rubric of ten criteria (maximum 300 points, threshold 165). A knock-out criterion applies: the scan must be carried out on a real workplace; without it, a pass is not possible regardless of the total score. For learners without a workplace, ADA sets up an example workplace. This exception is approved in advance by the Examination Committee and recorded in writing.

9. Exemptions and Recognition of Prior Learning (RPL)

9.1 General principle

Amsterdam Data Academy recognises that learners may already possess relevant knowledge or skills through prior education or professional experience. Exemptions may be granted when prior learning demonstrably covers the same learning outcomes as (part of) a programme.

9.2 Programme-based exemptions

Learners who have completed B02 (Data & Analytics) or B05 (AI Engineering) may receive exemptions for corresponding tracks within the B11 full programme
Learners who have completed B01 (Datascience & Python) or B04 (Data Engineering) may receive exemptions within the B06 (Data Expert) programme
Learners who have completed B05 (AI Engineering) or B07 (Applied AI) may receive exemptions for up to 50% of the B08 (AI Professional) programme

9.3 Entry based on professional experience

Learners with demonstrable professional experience may be admitted directly into Data Engineering or AI Engineering programmes. Decisions are based on documented evidence such as CVs, portfolios, certificates and interviews.

9.4 Exemption procedure

Requests for exemptions must be submitted before the start of the programme
Requests must include supporting documentation
The Examination Committee evaluates each request individually
Decisions are communicated in writing prior to enrolment

10. Examination Committee

Amsterdam Data Academy appoints an independent Examination Committee responsible for safeguarding assessment quality, reviewing exemption requests, monitoring grading consistency, and handling assessment-related disputes.

The Chair is external and independent, without any employment, financial or managerial relationship with ADA. Internal members abstain from decisions on the assessment of modules they teach. The committee meets at least four times per year and decides by majority vote. There is a vacancy for an additional external member, preferably with expertise in information security.

Rob Stroober

Chair (External)

Lecturer in Computer Science at the Amsterdam University of Applied Sciences (HvA). Expert in ICT education and curriculum design.

LinkedIn →

Roos Baak

Member (Internal)

Learning Manager at Amsterdam Data Academy. Responsible for curriculum management and examiner for the ADA bootcamps. Does not teach within B14.

Ákos Steger

Member (Internal)

Data Scientist and AI instructor at Amsterdam Data Academy. Specialist in language technologies, NLP and RAG applications. Over a decade of teaching experience. Abstains from decisions on the B14 modules he teaches.

LinkedIn →

Vacancy

Member (External)

Open position for an additional external member, preferably with expertise in information security.

11. Complaints, Objections and Appeals

Amsterdam Data Academy maintains two separate procedures:

Procedure 1: Complaints and disputes (service, organisation, guidance)

The learner discusses the complaint with the instructor or programme coordinator.
If unresolved, the learner may escalate in writing to ADA management. Management responds in writing within four weeks.
If not resolved after internal handling, the learner may escalate to the independent NRTO Dispute Committee (external, binding). Details: nrto.nl/geschillencommissie

Procedure 2: Objections and appeals (examination decisions)

Learners who disagree with an assessment decision may submit a written objection to the Examination Committee within 10 working days. The committee reconsiders and responds in writing within 4 weeks.
If the objection is not resolved satisfactorily, the learner may appeal to ADA’s independent Appeals Committee (Commissie van Beroep):
Eveline de Beer, Chair (external). Advocaat, gespecialiseerd in arbeidsrecht en sociaal zekerheidsrecht. Werkzaam bij Snijders Advocaten. LinkedIn
Patrick van Dolder, Member (external). Business Controller bij DMP (Broad Horizon groep). LinkedIn
The Appeals Committee convenes within 4 weeks and issues a binding decision within 6 weeks.

The full complaints procedure is published at: amsterdamdataacademy.com/complaint-procedure/

12. Separation of Teaching and Examination

Amsterdam Data Academy ensures the independence and objectivity of the examination process through a clear separation between teaching, administering and grading:

Teaching: Instructors deliver the programme and guide learners, but do not independently assess their own candidates
Administering: Assessments are submitted digitally via the learning platform (LearnDash). The platform records results automatically, preventing any interference
Grading: Assessments are reviewed by at least two assessors (four-eyes principle). At least one assessor is independent of the module. All grades are recorded in the platform and are auditable by the Examination Committee
Quality assurance: The Examination Committee formally approves all rubrics, assessment criteria and final results. The committee operates independently of programme delivery

13. Validity of Results

Module results are valid for a maximum of 3 years within the programme. After this period, the Examination Committee may require a new assessment.

14. Fraud and Irregularities

In cases of fraud or plagiarism, the Examination Committee may invalidate results. Serious cases may lead to exclusion from the programme.

15. Diploma and Certification

A programme is successfully completed when all module assessments, final assignments and, where applicable, the internship have been passed. The Examination Committee formally decides on diploma award. The diploma is signed by the Chair of the Examination Committee. The B11 Applied AI & Datascience programme is classified at NLQF Level 6. The programme will also be eligible for the SLIM-scholingssubsidie (40% employer subsidy). The B14 Cybersecurity en Digitale Soevereiniteit in het AI-tijdperk programme has been submitted for classification at NLQF Level 4; the decision is pending.

16. Continuity Guarantee

In the event Amsterdam Data Academy discontinues a programme, learners are guaranteed to complete their track within 12 months via ADA’s own teaching team under supervision of the Examination Committee, and/or via a designated partner institution.

17. Accreditation & Quality Marks

CRKBO: Central Register Short Vocational Education (VAT-exempt status)
NRTO: Dutch association for training institutions (quality keurmerk)
CPION: Independent audit body (annual quality audit, December 2025: no findings)
NLQF Level 6: the B11 Applied AI & Datascience programme is classified at NLQF Level 6
NLQF Level 4: the B14 Cybersecurity en Digitale Soevereiniteit in het AI-tijdperk programme has been submitted for classification at NLQF Level 4 (decision pending)

18. Publication and Updates

These Examination and Assessment Regulations are published on the Amsterdam Data Academy website
Learners receive access to these regulations at the start of their programme via the learning platform
The complete quality handbook (ADA Instructor Excellence Guide) is available to instructors and examiners
Updates are versioned and communicated transparently

Amsterdam Data Academy · Science Park 400, 1098 XH Amsterdam · 085 004 9642 CRKBO-registered · NRTO-certified · KvK: 90490592

Examination and assessment regulations