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For more information on your end-point assessment, contact Izaak.
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Artificial Intelligence (AI) Data Specialist – Apprentice assessment journey
| Phase & indicative timing* | Apprentice task | Key points & deadlines | |
|---|---|---|---|
| 1. | On-programme(≈ 24 months before Gateway) | Work towards the occupational standard | Minimum 12 months, typically 24 months. |
| 2. | Gateway | Employer confirms occupational competence & submits Gateway evidence | Employer signs off that you are consistently working at or above the standard. |
| Submit project brief to EPAO | Hand in brief of your work-based AI project at Gateway. | ||
| EPA clock starts – six-month window | The entire End-Point Assessment (EPA) must normally be finished within 6 months of Gateway. | ||
| 3. | EPA – Project Report, Presentation & Q&A | Receive report title from EPAO | EPAO issues title within 2 weeks of getting your brief. |
| Write & submit Project Report | 6 weeks to complete (5 000 ± 10 % words) after title confirmed. | ||
| Prepare & submit Presentation | 8 weeks from title to submit slides/media (overlaps with report window). | ||
| Deliver Presentation + supplementary questioning | 75 min session (≈ 30 min talk + 45 min Qs).EPAO gives 2 weeks’ notice and assessor must have the report ≥ 1 week beforehand. | ||
| 4. | EPA – Professional Discussion | Attend Professional Discussion | 60 min, one-to-one with independent assessor. EPAO gives 2 weeks’ notice. |
| 5. | EPA – Technical Test | Sit closed-book Technical Test | 4 long-answer scenario questions, up to 100 minutes. |
| 6. | Results & possible re-sits | Await grading (Fail / Pass / Merit / Distinction) | All three methods must be passed for an overall pass. Order of methods can vary. |
| If any method is failed, arrange re-sit or re-take | New Project Report = 6 weeks; new Presentation = +2 weeks; all within the original 6-month EPA window. |
Mapping of Knowledge, Skills and Behaviours (KSBs)
| Code | What the apprentice must know |
|---|---|
| K1 | How to use AI and machine-learning methodologies (data-mining, supervised/unsupervised ML, NLP, machine vision) to meet business objectives |
| K3 | How to apply advanced statistical and mathematical methods to commercial projects |
| K5 | How to design and deploy effective techniques of data analysis and research to meet business and customer needs |
| K6 | How data products can be delivered to engage the customer, organise information or solve a business problem, using iterative/incremental development and project-management approaches |
| K13 | How to identify the compromises and trade-offs that must be made when translating theory into practice in the workplace |
| K14 | The business value of a data product that can deliver the solution in line with business needs, quality standards and timescales |
| K23 | The use of different performance and accuracy metrics for model validation in AI projects |
| K26 | The scientific method and its application in research and business contexts, including experiment design and hypothesis testing |
| K28 | How to communicate concepts and present in a manner appropriate to diverse audiences, adapting techniques accordingly |
| Code | What the apprentice must be able to do |
|---|---|
| S2 – S5 | Analyse and evaluate test data; critically evaluate arguments and data; communicate concepts; manage stakeholder expectations |
| S7 | Work autonomously and interact effectively within wide, multidisciplinary teams |
| S9 – S11 | Manipulate, analyse and visualise complex datasets; select datasets and methodologies; apply advanced maths and statistics |
| S15 & S17–S18 | Build and maintain AI/data-science services; implement data curation and quality controls; develop data-system visualisation tools |
| S22 | Apply scientific methods (experimental design, EDA, hypothesis testing) for business decisions |
| S24 & S25 | Apply research/project-management techniques; select & use programming languages and follow good software-development practice |
| S27 | Analyse information, frame questions and discuss with subject-matter experts to scope new AI/data-science requirements |
| Code | Description |
|---|---|
| B2 | Reliable, objective and capable of independent and team working |
| B6 | Comfortable and confident interacting with people from technical and non-technical backgrounds; presents data and conclusions truthfully |
Mapping of Knowledge, Skills and Behaviours (KSBs)
| Code | What the apprentice must know |
|---|---|
| K7 | How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance and usability testing |
| K8 | How to interpret organisational policies, standards and guidelines in relation to AI and data |
| K10 | How own role fits with, and supports, organisational strategy and objectives |
| K11 | The roles and impact of AI, data science and data engineering in industry and society |
| K16 | High-performance computer architectures and their effective use |
| K17 | How to identify current industry trends across AI and data science and apply them |
| K18 | Programming languages and techniques applicable to data engineering |
| K19 | Principles and properties behind statistical and machine-learning methods |
| K21 | How AI/data-science techniques support and enhance the work of other analytical-team members |
| K22 | Relationship between mathematical principles and core AI/data-science techniques in the organisational context |
| K25 | Programming languages and modern ML libraries for commercially beneficial scientific analysis and simulation |
| K29 | The need for accessibility for all users and diversity of user needs |
| Code | What the apprentice must be able to do |
|---|---|
| S1 | Use applied research and data modelling to design/refine secure, stable, scalable data architectures |
| S6 | Provide direction and technical guidance on AI/data-science opportunities |
| S8 | Coordinate, negotiate and manage expectations of diverse stakeholders and suppliers |
| S12 | Consider regulatory, legal, ethical and governance issues when evaluating choices throughout the data process |
| S14 | Work collaboratively with software engineers to ensure suitable testing and documentation |
| S16 | Define requirements for—and supervise—implementation and use of data-management infrastructure (enterprise, private & public cloud) |
| S19 | Use scalable infrastructures, high-performance networks and services management to generate effective business solutions |
| S20 | Design efficient algorithms for accessing and analysing large data (incl. APIs to databases/datasets) |
| S23 | Disseminate AI/data-science practice across departments and industry; promote professional development and best practice |
| S26 | Select and apply the most effective AI/data-science techniques to solve complex business problems |
| S28 | Make independent, impartial decisions, respecting others’ views in complex, unpredictable circumstances |