{"id":57689,"date":"2025-12-13T15:45:00","date_gmt":"2025-12-13T14:45:00","guid":{"rendered":"https:\/\/amsterdamdataacademy.com\/?page_id=57689"},"modified":"2026-04-07T12:58:29","modified_gmt":"2026-04-07T10:58:29","slug":"examination-regulations","status":"publish","type":"page","link":"https:\/\/amsterdamdataacademy.com\/nl\/examination-regulations\/","title":{"rendered":"Examination regulations"},"content":{"rendered":"\n<p><!-- \n  EXAMINATION REGULATIONS \u2014 Amsterdam Data Academy\n  Updated: March 2026\n  WordPress page: \/examination-regulations\/\n\n  INSTRUCTIONS FOR WORDPRESS:\n  1. Go to Pages > Examination Regulations > Edit\n  2. Switch to \"Text\" or \"Code editor\" mode\n  3. Replace ALL content below the page title with this HTML\n  4. Save & Preview before publishing\n--><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-examination-and-assessment-regulations\"><strong>Examination and Assessment Regulations<\/strong><\/h2>\n\n\n\n<p><strong>Amsterdam Data Academy<\/strong><br><em>Version 2.0 \u2014 March 2026<\/em><br>Approved by: Amsterdam Data Academy Management &amp; Examination Committee<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-1-purpose\">1. Purpose<\/h3>\n\n\n\n<p>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.<\/p>\n\n\n\n<p>These regulations apply to all learners enrolled in courses and bootcamps offered by Amsterdam Data Academy, including the B11 \u2014 Applied AI &amp; Datascience full programme.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-scope\">2. Scope<\/h3>\n\n\n\n<p>These regulations apply to all programmes, including but not limited to:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Code<\/th><th class=\"has-text-align-left\" data-align=\"left\">Programme<\/th><th class=\"has-text-align-left\" data-align=\"left\">Type<\/th><\/tr><\/thead><tbody><tr><td><strong>B11<\/strong><\/td><td><strong>Applied AI &amp; Datascience<\/strong><\/td><td>Full programme<\/td><\/tr><tr><td>B01<\/td><td>Datascience &amp; Python<\/td><td>Bootcamp<\/td><\/tr><tr><td>B02<\/td><td>Data &amp; Analytics<\/td><td>Bootcamp<\/td><\/tr><tr><td>B03<\/td><td>Data Professional (Analytics &amp; Datascience)<\/td><td>Bootcamp<\/td><\/tr><tr><td>B04<\/td><td>Data Engineering<\/td><td>Bootcamp<\/td><\/tr><tr><td>B05<\/td><td>AI Engineering<\/td><td>Bootcamp<\/td><\/tr><tr><td>B06<\/td><td>Data Expert (Datascience &amp; Data Engineering)<\/td><td>Bootcamp<\/td><\/tr><tr><td>B07<\/td><td>Applied AI for Business Professionals<\/td><td>Bootcamp<\/td><\/tr><tr><td>B08<\/td><td>AI Professional (Applied AI &amp; AI Engineering)<\/td><td>Bootcamp<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Programme-specific details such as learning outcomes, duration and assessment formats are described in the relevant study guides.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-assessment-principles\">3. Assessment Principles<\/h3>\n\n\n\n<p>Assessments at Amsterdam Data Academy are designed according to the following principles:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>All assessments are aligned with published learning outcomes<\/li>\n\n\n\n<li>Assessment methods are valid, reliable and transparent<\/li>\n\n\n\n<li>Learners are assessed on knowledge, skills and professional application<\/li>\n\n\n\n<li>Assessment criteria and rubrics are communicated in advance via the learning platform<\/li>\n\n\n\n<li>Assessments are designed to support learning as well as evaluation<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-forms-of-assessment\">4. Forms of Assessment<\/h3>\n\n\n\n<p>Depending on the programme, assessment may include one or more of the following:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Practical assignments<\/strong> \u2014 real-world business cases or technical projects (e.g. Jupyter notebooks, dashboards, architecture reports)<\/li>\n\n\n\n<li><strong>Case studies<\/strong> \u2014 applied analysis of a concrete business scenario<\/li>\n\n\n\n<li><strong>Presentations<\/strong> \u2014 live or recorded delivery (max 10 slides or 3-minute demo)<\/li>\n\n\n\n<li><strong>Final assignments<\/strong> \u2014 integrated capstone projects per bootcamp track<\/li>\n\n\n\n<li><strong>Internship report &amp; presentation<\/strong> \u2014 including written report, stakeholder presentation and supervisor evaluation<\/li>\n<\/ul>\n\n\n\n<p>The assessment structure and weighting per programme are described in the study guide and assessment matrix.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5-grading-and-passing-criteria-cesuur\">5. Grading and Passing Criteria (Cesuur)<\/h3>\n\n\n\n<p>Every module is assessed using a <strong>4-point rubric scale<\/strong> across weighted criteria, producing a score out of 300 points.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Score<\/th><th class=\"has-text-align-left\" data-align=\"left\">Label<\/th><th class=\"has-text-align-left\" data-align=\"left\">Percentage<\/th><th class=\"has-text-align-left\" data-align=\"left\">Result<\/th><\/tr><\/thead><tbody><tr><td>3 \u00b7 Good<\/td><td>Pass with Distinction<\/td><td>\u2265 85% (\u2265 255\/300)<\/td><td><strong>Excellent<\/strong><\/td><\/tr><tr><td>2 \u00b7 Sufficient<\/td><td>Pass<\/td><td>55\u201384% (165\u2013254)<\/td><td><strong>Diploma eligible<\/strong><\/td><\/tr><tr><td>1 \u00b7 Needs Work<\/td><td>Resubmission<\/td><td>35\u201354% (105\u2013164)<\/td><td>One resit allowed<\/td><\/tr><tr><td>0 \u00b7 Insufficient<\/td><td>Fail<\/td><td>&lt; 35% (&lt; 105)<\/td><td>Module resit required<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Passing threshold (cesuur):<\/strong> A score of <strong>\u2265 165 out of 300 points (55%)<\/strong> is required to pass each module assessment. A score of \u2265 210\/300 (70%) qualifies as Good; \u2265 255\/300 (85%) qualifies as Excellent.<\/p>\n\n\n\n<p>The detailed assessment rubrics per bootcamp \u2014 including all criteria, weights, and descriptions per score level \u2014 are published in a separate document: <a href=\"https:\/\/amsterdamdataacademy.com\/wp-content\/uploads\/2026\/04\/ADA_Assessment_Rubrics.pdf\">https:\/\/amsterdamdataacademy.com\/wp-content\/uploads\/2026\/04\/ADA_Assessment_Rubrics.pdf<\/a><\/p>\n\n\n\n<p><strong>Diploma requirements (B11 programme):<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Achieve Sufficient (\u2265 165\/300) on all module assessments<\/li>\n\n\n\n<li>Achieve Sufficient on all five Final Assignments (A51\u2013A55)<\/li>\n\n\n\n<li>Complete the internship with minimum Sufficient on all criteria<\/li>\n\n\n\n<li>Attend minimum 80% of live sessions<\/li>\n\n\n\n<li>Submit all portfolio items by stated deadlines<\/li>\n<\/ul>\n\n\n\n<p><strong>Diploma with Distinction (Cum Laude):<\/strong> Learners achieving Good on the internship AND at least 3 track Final Assignments receive the diploma cum laude.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-6-four-eyes-principle\">6. Four-Eyes Principle<\/h3>\n\n\n\n<p>Amsterdam Data Academy applies a mandatory <strong>four-eyes principle (vier-ogenbeginsel)<\/strong> for all assessments. Every module has a designated <strong>teacher<\/strong> (begeleider) and an independent <strong>examiner<\/strong> (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.<\/p>\n\n\n\n<p>The Examination Committee oversees the examination process and ensures quality and consistency across all assessments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-7-resits-and-retakes\">7. Resits and Retakes<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Learners scoring <strong>1 (Needs Improvement)<\/strong> receive <strong>one resit opportunity<\/strong> per module. Resit assignments are new versions of the original task at equivalent difficulty.<\/li>\n\n\n\n<li>The examiner for the resit is <strong>independent<\/strong> of the original assessment.<\/li>\n\n\n\n<li>Learners scoring <strong>0 (Insufficient)<\/strong> on a Final Assignment must repeat the full track.<\/li>\n\n\n\n<li>For the internship, a structured improvement assignment is offered after an insufficient evaluation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-8-final-projects-and-practical-assignments\">8. Final Projects and Practical Assignments<\/h3>\n\n\n\n<p>Each bootcamp track concludes with a <strong>Final Assignment<\/strong> \u2014 an integrated case project demonstrating mastery. The B11 programme uses the <strong>Vivino case<\/strong> 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).<\/p>\n\n\n\n<p>Final projects are assessed using a detailed rubric and reviewed by <strong>two assessors<\/strong> (four-eyes principle).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-9-exemptions-and-recognition-of-prior-learning-rpl\">9. Exemptions and Recognition of Prior Learning (RPL)<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-9-1-general-principle\">9.1 General principle<\/h4>\n\n\n\n<p>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.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-9-2-programme-based-exemptions\">9.2 Programme-based exemptions<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Learners who have completed <strong>B02 (Data &amp; Analytics)<\/strong> or <strong>B05 (AI Engineering)<\/strong> may receive exemptions for corresponding tracks within the B11 full programme<\/li>\n\n\n\n<li>Learners who have completed <strong>B01 (Datascience &amp; Python)<\/strong> or <strong>B04 (Data Engineering)<\/strong> may receive exemptions within the <strong>B06 (Data Expert)<\/strong> programme<\/li>\n\n\n\n<li>Learners who have completed <strong>B05 (AI Engineering)<\/strong> or <strong>B07 (Applied AI)<\/strong> may receive exemptions for up to 50% of the <strong>B08 (AI Professional)<\/strong> programme<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-9-3-entry-based-on-professional-experience\">9.3 Entry based on professional experience<\/h4>\n\n\n\n<p>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.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-9-4-exemption-procedure\">9.4 Exemption procedure<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Requests for exemptions must be submitted <strong>before the start<\/strong> of the programme<\/li>\n\n\n\n<li>Requests must include supporting documentation<\/li>\n\n\n\n<li>The Examination Committee evaluates each request individually<\/li>\n\n\n\n<li>Decisions are communicated in writing prior to enrolment<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-10-examination-committee\">10. Examination Committee<\/h3>\n\n\n\n<p>Amsterdam Data Academy appoints an independent Examination Committee responsible for safeguarding assessment quality, reviewing exemption requests, monitoring grading consistency, and handling assessment-related disputes.<\/p>\n\n\n\n<p>All members operate <strong>independently<\/strong> \u2014 without financial or managerial responsibility at ADA. The committee meets at least four times per year and decides by majority vote.<\/p>\n\n\n\n<p><!-- Examination Committee members --><\/p>\n\n\n\n<div style=\"display: flex; flex-wrap: wrap; gap: 30px; margin: 2em 0;\">\n<div style=\"flex: 1; min-width: 200px; max-width: 280px; text-align: center;\"><img decoding=\"async\" style=\"width: 140px; height: 140px; border-radius: 50%; object-fit: cover; border: 3px solid #31399C; margin-bottom: 12px;\" src=\"https:\/\/amsterdamdataacademy.com\/wp-content\/uploads\/2026\/03\/michelle-brand.jpeg\" alt=\"Michelle Brand\">\n<h4 style=\"margin: 0 0 4px 0; color: #31399c;\">Michelle Brand<\/h4>\n<p style=\"margin: 0 0 4px 0; font-weight: 600;\">Chair (External)<\/p>\n<p style=\"margin: 0; font-size: 0.9em; color: #555;\">Founder, Brand New Learning. Specialist in learning design, quality assurance and educational programme development.<\/p>\n<p style=\"margin-top: 8px;\"><a style=\"color: #31399c;\" href=\"https:\/\/www.linkedin.com\/in\/michelleebrand\/\" target=\"_blank\" rel=\"noopener\">LinkedIn \u2192<\/a><\/p>\n<\/div>\n<div style=\"flex: 1; min-width: 200px; max-width: 280px; text-align: center;\"><img decoding=\"async\" style=\"width: 140px; height: 140px; border-radius: 50%; object-fit: cover; border: 3px solid #31399C; margin-bottom: 12px;\" src=\"https:\/\/amsterdamdataacademy.com\/wp-content\/uploads\/2026\/03\/Rob-stroober.jpeg\" alt=\"Rob Stroober\">\n<h4 style=\"margin: 0 0 4px 0; color: #31399c;\">Rob Stroober<\/h4>\n<p style=\"margin: 0 0 4px 0; font-weight: 600;\">Member (External)<\/p>\n<p style=\"margin: 0; font-size: 0.9em; color: #555;\">Lecturer in Computer Science at the Amsterdam University of Applied Sciences (HvA). Expert in ICT education and curriculum design.<\/p>\n<p style=\"margin-top: 8px;\"><a style=\"color: #31399c;\" href=\"https:\/\/www.linkedin.com\/in\/rob-stroober-4535a6\/\" target=\"_blank\" rel=\"noopener\">LinkedIn \u2192<\/a><\/p>\n<\/div>\n<div style=\"flex: 1; min-width: 200px; max-width: 280px; text-align: center;\"><img decoding=\"async\" style=\"width: 140px; height: 140px; border-radius: 50%; object-fit: cover; border: 3px solid #31399C; margin-bottom: 12px;\" src=\"https:\/\/amsterdamdataacademy.com\/wp-content\/uploads\/2025\/08\/Akos-Steger-scaled.jpg\" alt=\"\u00c1kos Steger\">\n<h4 style=\"margin: 0 0 4px 0; color: #31399c;\">\u00c1kos Steger<\/h4>\n<p style=\"margin: 0 0 4px 0; font-weight: 600;\">Member (Internal)<\/p>\n<p style=\"margin: 0; font-size: 0.9em; color: #555;\">Data Scientist and AI instructor at Amsterdam Data Academy. Specialist in language technologies, NLP and RAG applications. Over a decade of teaching experience.<\/p>\n<p style=\"margin-top: 8px;\"><a style=\"color: #31399c;\" href=\"https:\/\/www.linkedin.com\/in\/akossteger\/\" target=\"_blank\" rel=\"noopener\">LinkedIn \u2192<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading font-claude-response-body break-words whitespace-normal leading-[1.7]\" id=\"h-11-complaints-objections-and-appeals\">11. Complaints, Objections and Appeals<\/h3>\n\n\n\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">Amsterdam Data Academy maintains two separate procedures:<\/p>\n\n\n\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"><strong>Procedure 1 \u2014 Complaints and disputes (service, organisation, guidance)<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list [li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3\">\n<li>The learner discusses the complaint with the instructor or programme coordinator.<\/li>\n\n\n\n<li>If unresolved, the learner may escalate in writing to ADA management. Management responds in writing within four weeks.<\/li>\n\n\n\n<li>If not resolved after internal handling, the learner may escalate to the independent <strong>NRTO Dispute Committee<\/strong> (external, binding). Details: <a href=\"https:\/\/www.nrto.nl\/geschillencommissie\" class=\"underline underline underline-offset-2 decoration-1 decoration-current\/40 hover:decoration-current focus:decoration-current\">nrto.nl\/geschillencommissie<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\"><strong>Procedure 2 \u2014 Objections and appeals (examination decisions)<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list [li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3\">\n<li>Learners who disagree with an assessment decision may submit a written objection to the Examination Committee <strong>within 10 working days<\/strong>. The committee reconsiders and responds in writing within 4 weeks.<\/li>\n\n\n\n<li>If the objection is not resolved satisfactorily, the learner may appeal to ADA&#8217;s independent <strong>Appeals Committee (Commissie van Beroep)<\/strong>:<br><ul class=\"[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-disc flex flex-col gap-1 pl-8 mb-3\"><br><li class=\"whitespace-normal break-words pl-2\"><strong>Eveline de Beer<\/strong> \u2014 Chair (external). Advocaat, gespecialiseerd in arbeidsrecht en sociaal zekerheidsrecht. Werkzaam bij Snijders Advocaten. \u2014 <a href=\"https:\/\/www.linkedin.com\/in\/mrevelinedebeer\/\" class=\"underline underline underline-offset-2 decoration-1 decoration-current\/40 hover:decoration-current focus:decoration-current\">LinkedIn<\/a><\/li><br><li class=\"whitespace-normal break-words pl-2\"><strong>Patrick van Dolder<\/strong> \u2014 Member (external). Business Controller bij DMP (Broad Horizon groep). \u2014 <a href=\"https:\/\/www.linkedin.com\/in\/patrick-van-dolder-6773a012b\" class=\"underline underline underline-offset-2 decoration-1 decoration-current\/40 hover:decoration-current focus:decoration-current\">LinkedIn<\/a><\/li><\/ul><\/li>\n\n\n\n<li><p>The Appeals Committee convenes within 4 weeks and issues a binding decision within 6 weeks.<\/p><\/li>\n<\/ol>\n\n\n\n<p class=\"font-claude-response-body break-words whitespace-normal leading-[1.7]\">The full complaints procedure is published at: <a class=\"underline underline underline-offset-2 decoration-1 decoration-current\/40 hover:decoration-current focus:decoration-current\" href=\"https:\/\/amsterdamdataacademy.com\/complaint-procedure\/\">amsterdamdataacademy.com\/complaint-procedure\/<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-12-separation-of-teaching-and-examination\">12. Separation of Teaching and Examination<\/h3>\n\n\n\n<p>Amsterdam Data Academy ensures the independence and objectivity of the examination process through a clear separation between teaching, administering and grading:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Teaching:<\/strong> Instructors deliver the programme and guide learners, but do not independently assess their own candidates<\/li>\n\n\n\n<li><strong>Administering:<\/strong> Assessments are submitted digitally via the learning platform (LearnDash). The platform records results automatically, preventing any interference<\/li>\n\n\n\n<li><strong>Grading:<\/strong> 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<\/li>\n\n\n\n<li><strong>Quality assurance:<\/strong> The Examination Committee formally approves all rubrics, assessment criteria and final results. The committee operates independently of programme delivery<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-13-validity-of-results\">13. Validity of Results<\/h3>\n\n\n\n<p>Module results are valid for a maximum of <strong>3 years<\/strong> within the programme. After this period, the Examination Committee may require a new assessment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-14-fraud-and-irregularities\">14. Fraud and Irregularities<\/h3>\n\n\n\n<p>In cases of fraud or plagiarism, the Examination Committee may invalidate results. Serious cases may lead to exclusion from the programme.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-15-diploma-and-certification\">15. Diploma and Certification<\/h3>\n\n\n\n<p>The programme is successfully completed when all module assessments, final assignments and the internship have been passed. The Examination Committee formally decides on diploma award. The diploma is signed by the Chair of the Examination Committee.<\/p>\n\n\n\n<p>The B11 \u2014 Applied AI &amp; Datascience programme has been submitted for NLQF Level 6 classification (bachelor equivalent). Upon classification, the programme will be eligible for the <strong>SLIM-scholingssubsidie<\/strong> (40% employer subsidy).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-16-continuity-guarantee\">16. Continuity Guarantee<\/h3>\n\n\n\n<p>In the event Amsterdam Data Academy discontinues a programme, learners are guaranteed to complete their track within <strong>12 months<\/strong> via ADA&#8217;s own teaching team under supervision of the Examination Committee, and\/or via a designated partner institution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-17-accreditation-amp-quality-marks\">17. Accreditation &amp; Quality Marks<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>CRKBO<\/strong> \u2014 Central Register Short Vocational Education (VAT-exempt status)<\/li>\n\n\n\n<li><strong>NRTO<\/strong> \u2014 Dutch association for training institutions (quality keurmerk)<\/li>\n\n\n\n<li><strong>CPION<\/strong> \u2014 Independent audit body (annual quality audit, December 2025: no findings)<\/li>\n\n\n\n<li><strong>NLQF Level 6<\/strong> \u2014 classification submitted (bachelor equivalent, pending)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-18-publication-and-updates\">18. Publication and Updates<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>These Examination and Assessment Regulations are published on the Amsterdam Data Academy website<\/li>\n\n\n\n<li>Learners receive access to these regulations at the start of their programme via the learning platform<\/li>\n\n\n\n<li>The complete quality handbook (<em>ADA Instructor Excellence Guide<\/em>) is available to instructors and examiners<\/li>\n\n\n\n<li>Updates are versioned and communicated transparently<\/li>\n<\/ul>\n\n\n\n<p>Amsterdam Data Academy \u00b7 Science Park 400, 1098 XH Amsterdam \u00b7 085 004 9642<br>CRKBO-registered \u00b7 NRTO-certified \u00b7 KvK: 90490592<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Examination and Assessment Regulations Amsterdam Data AcademyVersion 2.0 \u2014 March 2026Approved by: Amsterdam Data Academy Management &amp; 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 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-57689","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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