“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
AI-Ready Data and Knowledge Engineering with Fabric
Prepare trustworthy structured and document context for downstream AI consumers. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSPractitioner / specialist
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration48 hours
FormatPrivate cohort
LevelPractitioner / specialist
Course CodeD03
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers AI-Ready Data and Knowledge Engineering with Fabric as an MCT-led VNode ITeS program for Data engineers, Analytics engineers, Knowledge owners. Typical duration is 48 hours (Private cohort). Delivery stays scoped to how your teams will actually use the platform after the room empties. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Data engineers, Analytics engineers, Knowledge owners
- Tailored Team Delivery
Overview
Executive overview
Prepare trustworthy structured and document context for downstream AI consumers.
Environment and licensing
Fabric capacity and isolated workspace Author and consumer personas Source files and notebook runtime Data-agent labs: paid F2+ or qualifying P1+ capacity, tenant AI settings and source access as documented; cohort capacity sized separately Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Fabric foundations and practical PySpark
- AI and Generative AI Foundations, or equivalent demonstrable experience
- Microsoft Fabric Engineering Foundations, or equivalent demonstrable experience
- PySpark for Data Engineering, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Prepare traceable structured and document context in Fabric
- Prove coverage, quality, freshness and consumer reference checks
- Test permitted data visibility and source withdrawal propagation
- Provide a semantic and index-handoff contract with operating ownership
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
Proposed instructor-led duration; delivery subject to environment and lab validation.
- AI information requirements
- Governed ingestion and provenance
- Content preparation and quality
- Semantic context and permissions
- AI consumption and retrieval validation
- Knowledge engineering capstone
1Module 1 · 8 hours
AI information requirements
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Module 1 · 8 hours
AI information requirements
Map what an AI consumer needs and identify unsupported assumptions.
Consumer tasks
- Map consumer tasks to required facts and documents
- Separate analytical questions from document retrieval
Source inventory
- Inventory sources and authoritative owners
- Record missing or conflicting information
Access boundaries
- Define access and purpose boundaries
- Identify restricted and unnecessary information
Freshness and answer contracts
- Set freshness and completeness targets
- Choose reference questions and expected evidence
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: AI information requirements
Map what an AI consumer needs and identify unsupported assumptions.
Expected output. AI information requirements contract
Acceptance. The information contract specifies authoritative sources and testable consumption requirements.
2Module 2 · 8 hours
Governed ingestion and provenance
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Module 2 · 8 hours
Governed ingestion and provenance
Ingest synthetic content while retaining provenance and access metadata.
Documents and tables
- Preserve original source references during ingestion
- Choose stable identifiers across refreshes
Metadata
- Capture timestamps, versions and ownership
- Track changes without losing provenance
Stable identifiers
- Propagate permitted access metadata
- Avoid labelling metadata as enforcement by itself
Source ownership
- Retain processing and quality lineage
- Record withdrawn or superseded content
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Governed ingestion and provenance
Ingest synthetic content while retaining provenance and access metadata.
Expected output. Traceable source collection
Acceptance. Each context item can be traced to a current permitted source version.
3Module 3 · 8 hours
Content preparation and quality
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Module 3 · 8 hours
Content preparation and quality
Create validated context units and measure coverage and freshness.
Parsing
- Normalise documents and structured records
- Handle extraction errors without silent omission
Normalisation
- Choose chunk boundaries around meaningful content
- Retain titles, sections and source references
Chunk boundaries
- Identify duplicates and near-duplicate versions
- Resolve obsolete content according to ownership rules
Duplicate and stale content
- Measure source coverage and parsing completeness
- Assign ownership to unresolved quality exceptions
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Content preparation and quality
Create validated context units and measure coverage and freshness.
Expected output. Quality-controlled context dataset
Acceptance. The context dataset exposes parsing gaps, stale content and duplicate-handling decisions.
4Module 4 · 8 hours
Semantic context and permissions
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Module 4 · 8 hours
Semantic context and permissions
Define business context and test visibility using separate identities.
Business definitions
- Define business terms and metric meaning
- Resolve inconsistent entity names and relationships
Relationships
- Map structured context into semantic models
- Distinguish semantic context from a vector index
Semantic models
- Design user and workload access patterns
- Test restricted and allowed source visibility
Persona tests
- Define which system enforces each permission
- Avoid relying on model instructions for security
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Semantic context and permissions
Define business context and test visibility using separate identities.
Expected output. Semantic and permission specification
Acceptance. The semantic and permission specification is testable through named consumer personas.
5Module 5 · 8 hours
AI consumption and retrieval validation
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Module 5 · 8 hours
AI consumption and retrieval validation
Validate structured grounding and a documented document-index handoff; use Azure AI Search only if provisioned.
Structured query grounding
- Validate structured grounding against reference queries
- Identify ambiguous business terms and filters
Document retrieval export
- Specify a document-index export contract
- Include source, chunk, freshness and access metadata
Reference answers
- Use a provisioned external search service only when scoped
- Keep a non-index handoff validation route
External index boundaries
- Evaluate evidence relevance and source attribution
- Record unsupported consumer questions
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: AI consumption and retrieval validation
Validate structured grounding and a documented document-index handoff; use Azure AI Search only if provisioned.
Expected output. Consumption tests and index handoff contract
Acceptance. The output contract and reference tests validate both structured context and document handoff.
6Module 6 · 8 hours
Knowledge engineering capstone
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Module 6 · 8 hours
Knowledge engineering capstone
Demonstrate updated and withdrawn content propagating through the data product and consumer tests.
Refresh
- Demonstrate source update and reprocessing
- Preserve identifiers needed for downstream refresh
Deletion propagation
- Withdraw obsolete or restricted content
- Verify removal reaches each in-scope consumer
Quality gates
- Apply quality gates before publishing context
- Record exceptions and remediation ownership
Operational ownership
- Document refresh, access and support responsibilities
- Present measured readiness and remaining integration work
Concept and design 2h · applied exercise 4h · review 2h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Knowledge engineering capstone
Demonstrate updated and withdrawn content propagating through the data product and consumer tests.
Expected output. Assessed AI-ready Fabric knowledge product
Acceptance. Updates and withdrawals propagate through the assessed data product and its tests.
Capstone
Demonstrate updated and withdrawn content propagating through the data product and consumer tests. Assessed AI-ready Fabric knowledge product Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Prepare traceable structured and document context in Fabric 30% Prove coverage, quality, freshness and consumer reference checks 25% Test permitted data visibility and source withdrawal propagation 25% Provide a semantic and index-handoff contract with operating ownership 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed AI-ready Fabric knowledge product
- Evidence of relevant access, quality and risk controls
- Demonstration and limitations record
Scope boundaries
Step-by-step lab instructions; Runnable solution code; Packaged source datasets; Executed environment validation
Delivery options
Count teaching hours, not calendar days. Exclude breaks. Split into 4-hour or 8-hour teaching sessions; schedule complex project work in contiguous blocks.
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Private cohort delivery
- Practitioner / specialist practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Prepare trustworthy structured and document context for downstream AI consumers.
- Private cohort scheduling
- Equivalent-experience entry review
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
