---
title: "DP-600 Study Guide — Master the DP-600"
description: "An interactive study guide built on 7 memory techniques to help you pass the Microsoft Fabric Analytics Engineer Associate exam."
url: "https://lucidlabs.com.au/insights/dp-600"
---

# Master the DP-600

An interactive study guide built on 7 memory techniques to help you pass the Microsoft Fabric Analytics Engineer Associate exam.

Maintain 25-30%Prepare 45-50%Semantic 25-30%

What it covers

Semantic models, DAX, Direct Lake, query folding, security (RLS/OLS/CLS), deployment pipelines, and Power BI report optimisation within Microsoft Fabric.

Ideal for

BI developers, Power BI report authors, data analysts building semantic models, and analytics engineers working in Microsoft Fabric.

Aspire to this if

You're a data analyst wanting to move into analytics engineering, or a SQL developer looking to master DAX and the modern Microsoft BI stack.

Section 1 / Spatial Memory

## The Map

Tap any component to see what you need to know for the exam.

**🏢 OneLake**

Central Hub

**🏭 Data Factory**

Orchestration

**🧹 Dataflows Gen2**

Power Query Online

**📓 Notebooks**

PySpark · Spark SQL

**🏛️ Lakehouse**

Schema-on-read · Spark

**🏗️ Warehouse**

Schema-on-write · T-SQL

**⚡ Eventhouse**

Streaming · KQL

**🔗 SQL Analytics Endpoint**

Auto-generated · Read-only

**🧮 Semantic Model**

Direct Lake · Import · DirectQuery

**🎨 Power BI Reports**

Visualisation

Section 2 / Narrative Memory

## The Story

Follow the narrative to build a mental model of how everything connects.

📦

### The Arrival

Raw data arrives at the grand harbour of OneLake, the one lake to rule them all. Every piece of data in Microsoft Fabric automatically docks here -no exceptions. OneLake is built atop Azure Data Lake Storage Gen2, providing a single unified namespace for the entire organisation.

**Exam Intel**OneLake = OneDrive for data. One logical lake per tenant. Built on ADLS Gen2. All Fabric items store data in OneLake automatically.

🏗️

### The Factory Floor

Inside the Data Factory, pipelines hum with 170+ connectors pulling data from every corner of the enterprise. The Copy Activity moves raw materials from source to destination, while orchestration pipelines coordinate the entire operation.

**Exam Intel**Data Factory = orchestration + data movement. Copy Activity for transfers. Supports full and incremental loading patterns. Pipeline templates available.

🧹

### The Cleaning Crew

Dataflows Gen2 workers scrub, transform, and polish the data using Power Query Online. They watch the folding indicators carefully -5 states: Folding, Not Folding, Might Fold, Opaque, and Unknown. When steps fold, transformations push back to the source.

**Exam Intel**Query folding = pushing transforms to source. Check icons in Dataflow WEB UI (not desktop!). Column Quality/Distribution/Profile for data profiling. CSV files can't fold.

🔬

### The Laboratory

Data scientists in the Notebook Lab run PySpark experiments. They broadcast small DataFrames to every node, use withColumn to engineer features, and maintain their Delta tables with regular VACUUM and OPTIMIZE cycles.

**Exam Intel**PySpark key methods: broadcast(), .withColumn(), .cast(), df.summary(). Delta maintenance: VACUUM, OPTIMIZE, DESCRIBE HISTORY. Predict function works with Spark SQL and PySpark.

🏛️

### The Twin Vaults

Data reaches two great vaults. The Lakehouse welcomes all comers -structured, semi-structured, unstructured -with a flexible schema-on-read philosophy. The Warehouse, more selective, demands structured data and rewards it with full T-SQL power and multi-table transactions.

**Exam Intel**Lakehouse = schema-on-read, Spark-first, Delta tables. Warehouse = schema-on-write, T-SQL, multi-table transactions. Lakehouse for data engineering/ML. Warehouse for traditional BI/analytics.

🔗

### The Shortcut

A magical portal -the OneLake Shortcut -lets data appear in multiple places without being copied. Zero-copy access across workspaces, across lakehouses, even across clouds.

**Exam Intel**Shortcuts = zero-copy data access. Work across workspaces and external sources. No data duplication. Appear as regular folders/tables in the lakehouse.

🏗️

### The Architect's Workshop

The Semantic Model architect arranges tables into a perfect star schema -fact tables at the centre recording transactions and events, dimension tables radiating outward with descriptive attributes. Relationships flow one-to-many from dimension to fact, with single-direction cross-filtering keeping queries clean and predictable.

**Exam Intel**Star schema: fact tables (transactions) + dimension tables (attributes). Relationships: 1-to-many default, single cross-filter direction (dimension→fact). Bi-directional cross-filter only when needed (causes ambiguity). Many-to-many requires bridge table. USERELATIONSHIP() activates inactive relationships.

⚡

### The Power Source

Storage modes determine how the model draws its power. Import caches everything in memory for blistering speed. DirectQuery reaches through live portals to the source. Direct Lake -the newest and most elegant -reads Delta tables directly from OneLake without importing or querying. Composite models mix all three.

**Exam Intel**Import = in-memory cache, fastest, scheduled refresh. DirectQuery = live, no size limit, slower. Direct Lake = reads Delta parquet directly, near real-time, capacity guardrails. Composite = mix modes. Direct Lake fallback: Automatic (default), DirectLakeOnly, DirectQueryOnly.

📐

### The Formula Chamber

In the Formula Chamber, DAX expressions bring the model to life. CALCULATE -the most powerful function -transforms filter context, while context transition converts row context into filter context inside iterators. Time intelligence functions like TOTALYTD and SAMEPERIODLASTYEAR unlock temporal analysis, but only when the date table is properly marked.

**Exam Intel**CALCULATE modifies filter context -the #1 tested DAX concept. Context transition: row context becomes filter context inside CALCULATE/iterators. Time intelligence requires a marked date table with continuous dates. TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESYTD. Calculation groups use SELECTEDMEASURE(), precedence property, explicit measures only.

🛡️

### The Security Sentinels

Three sentinels guard the data. RLS filters rows with DAX expressions. CLS hides specific columns. OLS -the most powerful -makes entire tables and columns invisible, as if they never existed.

**Exam Intel**RLS = DAX filter expressions + USERPRINCIPALNAME(). CLS = column-level hiding. OLS = requires Tabular Editor, cascading effect on dependent measures, objects completely invisible. Key difference: RLS filters data, OLS hides metadata.

🎨

### The Gallery

Finally, data is displayed in the Gallery of Reports. The Performance Analyzer watches for slow visuals. Viewers browse with confidence, knowing Promoted items are team-approved, Certified items meet org quality standards, and Master data items are the organisation's single source of truth.

**Exam Intel**Performance Analyzer separates DAX query time from render time. Endorsement: Promoted (team) → Certified (org quality) → Master data (single source of truth). Sensitivity labels propagate downstream. Reduce visuals per page for performance.

🔄

### The Cycle Continues

Deployment pipelines move everything from Dev to Test to Prod. Git integration via PBIP format enables PR reviews. The XMLA endpoint opens the door to enterprise tools like Tabular Editor and DAX Studio.

**Exam Intel**Deployment pipelines: default Dev→Test→Prod (supports 2-10 stages) with data source binding rules. Git: PBIP/PBIR format. XMLA endpoint for Tabular Editor, SSMS, scripted deployments. Impact analysis traces downstream dependencies.

Section 3 / Acronym Memory

## Mnemonic Wall

Memorable acronyms and phrases to anchor key exam concepts in your memory.

🗺️

MPS

**M**aintain (25-30%), **P**repare (45-50%), **S**emantic models (25-30%)

The 3 exam domains (updated April 2026). Prepare is the biggest slice. Semantic models increased to 25-30%.

💾

DILD

**D**irect Lake, **I**mport, **L**ive connection, **D**irectQuery

Storage modes from newest to oldest.

🔒

ROC

**R**LS, **O**LS, **C**LS - ROC solid security

Security layers. RLS filters Rows, OLS hides Objects, CLS restricts Columns.

🪟

VOW

**V**AR/RETURN, **O**RDERBY, **W**INDOW

"I VOW to learn window functions." DAX window function building blocks.

🔢

SIX

**S**UMX, **I**terators end in X, multi-column requires **X**

"The SIX rule: if you need multiple columns, add an X." Iterator function rule.

🚀

DTP

**D**ev, **T**est, **P**rod

Deployment pipeline stages in order.

🛠️

TABS

**T**abular Editor, **A**LM Toolkit, **B**est Practice Analyzer, DAX **S**tudio

The 4 key external tools for Power BI enterprise management.

📊

QDP

Column **Q**uality, **D**istribution, **P**rofile

Data profiling tools in Dataflows Gen2. Quality = % valid/error/empty. Distribution = distinct vs unique counts. Profile = min/max/statistics.

⭐

STAR

**S**ingle direction, **T**ables = fact + dim, **A**void bi-directional, **R**elationships 1-to-many

Star schema design rules. Single cross-filter direction from dimension to fact. Avoid bi-directional unless absolutely needed.

🕐

CATS

**C**ALCULATE modifies context, **A**LL removes filters, **T**ime intel needs marked date table, **S**AMELPERIODLASTYEAR for YoY

Core DAX patterns. CALCULATE = context modifier. ALL/ALLSELECTED/ALLEXCEPT remove filters. Mark as Date Table required for time intelligence.

🔧

VOCO

**V**ACUUM, **O**PTIMIZE, **C**OMPACT, describe hist**O**ry

Delta table maintenance commands.

⭐

PCM

**P**romoted (team) → **C**ertified (org quality) → **M**aster data (single source of truth)

"PCM = Promoted → Certified → Master data." Three endorsement levels.

⚡

WISE

**W**here (filter), **I**dentify patterns, **S**ummarize (group), **E**xtend (add columns)

KQL operators in logical order.

⚡

FLASH

**F**allback triggers: RLS on tab**L**e, views (**A**uto-generated), capacity guard rail**S**, Direct Lake on OneLake **H**as no fallback

Direct Lake fallback triggers.

🔄

RR

**R**angeStart / **R**angeEnd

Incremental refresh parameters. Must be exact names. Query folding strongly recommended. No equality on both parameters. No IR for Direct Lake.

👥

AMVC

**A**dmin, **M**ember, **C**ontributor, **V**iewer

Workspace roles from most to least powerful.

🔑

BUILD

**B**uild permission **U**nlocks: **I**nvestigate in Excel, **L**ink to semantic models cross-workspace, **D**esign composite models

Build permission capabilities.

Section 4 / Contrast Memory

## Versus Arena

Side-by-side comparisons to sharpen your understanding of similar concepts.

vs

LakehousevsWarehouse

Click to compare

#### Lakehouse vs Warehouse

| Aspect | Lakehouse | Warehouse |
| --- | --- | --- |
| Schema | Schema-on-read | Schema-on-write |
| Language | Spark (PySpark/SQL) | T-SQL |
| Data types | All (structured, semi, unstructured) | Structured only |
| Transactions | Single table | Multi-table |
| Best for | Data engineering, ML, data science | BI analytics, reporting |
| Storage | Delta tables + Files | Tables only |
| Auto-endpoint | SQL Analytics Endpoint (read-only) | Full SQL (read-write) |

Click to flip back

vs

ImportvsDirectQueryvsDirect Lake

Click to compare

#### Import vs DirectQuery vs Direct Lake

| Aspect | Import | DirectQuery | Direct Lake |
| --- | --- | --- | --- |
| Data location | In-memory cache | Source system / OneLake Delta |
| Performance | Fastest | Slowest / Fast (with fallback) |
| Freshness | Scheduled refresh | Real-time / Near real-time |
| Size limit | Model size limit | No limit / Capacity guardrails |
| Refresh needed | Yes | No / Framing only |
| Best for | Small-medium data | Must be live / Fabric-native data |

Click to flip back

vs

RLSvsCLSvsOLS

Click to compare

#### RLS vs CLS vs OLS

| Aspect | RLS | CLS | OLS |
| --- | --- | --- | --- |
| Restricts | Rows | Columns / Tables & Columns |
| Method | DAX filter expressions | Column hiding per role / Tabular Editor required |
| User sees | Object exists, data filtered | Column hidden / Object doesn't exist |
| Key function | USERPRINCIPALNAME() | N/A / N/A |
| Cascading | No | No / Yes -dependent measures hidden |
| Config in PBI Desktop | Yes | Yes / No -external tool only |

Click to flip back

vs

Direct Lake: OneLakevsSQL Endpoints

Click to compare

#### Direct Lake Source Comparison

| Aspect | Direct Lake: OneLake | SQL Endpoints |
| --- | --- | --- |
| Source | Delta parquet files in OneLake | Auto-generated SQL analytics endpoint |
| Performance | Best (direct file read) | Good (may fallback to DirectQuery) |
| Views support | No | Yes (SQL views) |
| Cross-DB queries | No | Yes |
| Best for | Lakehouse Delta tables | Warehouse data, need SQL views |
| Fallback risk | Lower | Higher (views trigger fallback) |

Click to flip back

vs

Dataflow Gen1vsGen2

Click to compare

#### Dataflow Gen1 vs Gen2

| Aspect | Dataflow Gen1 | Gen2 |
| --- | --- | --- |
| Output | Power BI dataset only | Multiple destinations |
| Engine | Power Query | Power Query + enhanced compute |
| Staging | Limited | Lakehouse staging |
| Performance | Standard | Fast copy + scale-out |
| Fabric native | No | Yes |

Click to flip back

vs

PromotedvsCertifiedvsMaster Data

Click to compare

#### Endorsement Levels

| Aspect | Promoted | Certified | Master Data |
| --- | --- | --- | --- |
| Who sets it | Users with write access | Designated certifiers / Designated certifiers |
| Meaning | "Team trusts this" | "Meets org quality standards" / "Single source of truth" |
| Governance | Informal | Formal process / Formal + authoritative |

Click to flip back

vs

MeasuresvsCalculated Columns

Click to compare

#### Measures vs Calculated Columns

| Aspect | Measures | Calculated Columns |
| --- | --- | --- |
| Evaluated | Query time (dynamic) | Refresh time (static) |
| Context | Filter context | Row context |
| Storage | No storage cost | Stored in model |
| Performance | Generally better | Increases model size |
| Use for | Aggregations, ratios, KPIs | Row-level flags, categories |
| Best practice | Prefer measures | Use sparingly |

Click to flip back

vs

CALCULATEvsNo CALCULATE

Click to compare

#### CALCULATE vs No CALCULATE

| Aspect | CALCULATE | No CALCULATE |
| --- | --- | --- |
| Context | Modifies filter context | Inherits current context |
| Transition | Converts row → filter context | No context transition |
| Filters | Can add/remove/replace filters | Uses existing filters only |
| Common with | ALL, FILTER, VALUES, time intel | Simple SUM, COUNT, AVERAGE |
| Use for | Ratios, totals ignoring slicers, YoY | Basic aggregations |
| Exam weight | Very heavily tested | Assumed knowledge |

Click to flip back

vs

SinglevsBi-directional Cross-filter

Click to compare

#### Cross-filter Direction

| Aspect | Single | Bi-directional Cross-filter |
| --- | --- | --- |
| Direction | Dimension → Fact only | Both directions |
| Default | Yes (recommended) | No (opt-in) |
| Performance | Better | Slower, more complex |
| Ambiguity risk | None | Can create ambiguous paths |
| Use when | Standard star schema | Many-to-many bridge tables, slicers from fact side |
| Best practice | Prefer this always | Only when required, document why |

Click to flip back

vs

AggregatorsvsIterators

Click to compare

#### Aggregators vs Iterators

| Aspect | Aggregators | Iterators |
| --- | --- | --- |
| Examples | SUM, AVERAGE, MIN, MAX | SUMX, AVERAGEX, MINX, MAXX |
| Naming | No suffix | End in X |
| Parameters | Single column | Table + expression |
| Multi-column | Cannot | Required for multi-column |
| Context | Filter context | Row context per row |
| Performance | Faster | Slower on large tables |

Click to flip back

vs

Query Folding: YesvsNo

Click to compare

#### Query Folding: Yes vs No

| Aspect | Query Folding: Yes | No |
| --- | --- | --- |
| Indicators | Folding (green) | Not folding / Might fold / Opaque / Unknown |
| Processing | At data source | Locally in Power Query |
| Performance | Fast, efficient | Slow, resource-intensive |
| Examples | SQL Server, Azure SQL | CSV files, web scraping |
| Required for | Incremental refresh | N/A |
| Check in | Dataflow WEB UI | (same) |

Click to flip back

vs

OneLake ShortcutsvsData Copy

Click to compare

#### OneLake Shortcuts vs Data Copy

| Aspect | OneLake Shortcuts | Data Copy |
| --- | --- | --- |
| Data duplication | No (zero-copy) | Yes |
| Storage cost | None | Double |
| Freshness | Always current | Stale until refreshed |
| Cross-workspace | Yes | Yes |
| External sources | Yes (ADLS, S3) | Via pipelines |
| Use when | Real-time access needed | Transformation required |

Click to flip back

Section 5 / Grouping Memory

## Cheat Sheet

Organised reference grouped by exam domain — everything you need on one page.

### Maintain a Data Analytics Solution

25-30%

#### Workspace Roles & Governance

-   Admin > Member > Contributor > Viewer
-   Build Permission: Create reports, Analyze in Excel, composite models, cross-workspace access
-   Sensitivity Labels: Public → General → Confidential → Highly Confidential. Propagate downstream
-   Endorsed: Promoted → Certified → Master data (single source of truth)
-   F-SKU (Fabric), Premium. Shared = PBI only

#### Deployment & Version Control

-   Deployment Pipelines: Dev → Test → Prod (supports 2-10 stages)
-   Data source binding rules per stage
-   Impact analysis for downstream dependencies
-   Git: PBIP/PBIR text format for PR reviews. Notebooks as source files
-   XMLA Endpoint: Tabular Editor, SSMS, partitioning, scripted deployments

#### Performance & Monitoring

-   Performance Analyzer: DAX query time vs visual render time
-   Query Diagnostics: backend DQ/DL behaviour
-   Fewer visuals per page. Summary over detail
-   Dropdowns not lists for high-cardinality slicers
-   Disable unnecessary cross-filtering between visuals

#### Security

-   RLS: DAX filter expressions + USERPRINCIPALNAME(). Test with "View as" role
-   OLS: Tabular Editor only. Objects completely invisible. Cascading effect on measures
-   CLS: Column-level hiding per role
-   Sensitivity labels propagate downstream to reports

#### Query Languages

-   T-SQL: warehouse + SQL analytics endpoint. Visual Query Editor for no-code
-   KQL: where, summarize, render, extend. Eventhouse/real-time analytics
-   DAX: EVALUATE + SUMMARIZECOLUMNS. CALCULATE for context modification
-   Lineage: Source → Dataflow → Lakehouse → Model → Reports

### Prepare Data

45-50%

#### OneLake & Shortcuts

-   One lake per tenant. ADLS Gen2. Unified namespace
-   Shortcuts: zero-copy, cross-workspace, cross-cloud (ADLS, S3)

#### Storage

-   Lakehouse: schema-on-read, Spark, Delta + Files, Z-Order, file-level security
-   Warehouse: schema-on-write, T-SQL, multi-table tx, cross-DB queries
-   Eventhouse: streaming/events, KQL, telemetry, logs, IoT
-   SQL Analytics Endpoint: auto-gen for lakehouses, read-only T-SQL

#### Data Movement

-   Data Factory: 170+ connectors, Copy Activity, full + incremental
-   Dataflows Gen2: Power Query Online, query folding (web UI icons!)
-   Profiling: Column Quality / Distribution / Profile

#### Query Folding

-   5 indicator states: Folding, Not Folding, Might Fold, Opaque, Unknown
-   Check in web UI only (not desktop)
-   CSV never folds. Required for incremental refresh

#### Notebooks & Delta

-   PySpark + Spark SQL. broadcast() for small DFs
-   .withColumn(), .cast(). predict() in both languages
-   VACUUM, OPTIMIZE, DESCRIBE HISTORY, Z-Order

### Semantic Models

20-25%

#### Star Schema Design

-   Fact tables: transactions/events (Sales, Orders). Dimension tables: descriptive attributes (Date, Product, Customer)
-   Relationships: 1-to-many from dimension to fact. Single cross-filter direction (default)
-   Bi-directional cross-filter: only for many-to-many bridge tables. Creates ambiguity risk
-   USERELATIONSHIP(): activates inactive relationships in CALCULATE. One active relationship per path
-   Avoid snowflake schema (chained dimensions) — flatten into star for best performance

#### Storage Modes

-   Import: fastest, in-memory, scheduled refresh. Best for small-medium data
-   DirectQuery: live queries to source, slower, no size limit. Best for must-be-live
-   Direct Lake: reads Delta parquet directly from OneLake. No import, no live query overhead
-   Composite: mix modes in one model. Remote models (chained) vs local tables
-   Large format: enable for >10GB OR any XMLA write operations

#### Direct Lake

-   Fallback modes: Automatic (default), DirectLakeOnly (errors instead of fallback), DirectQueryOnly
-   Fallback triggers: RLS on table, views (auto-generated SQL endpoint), capacity guardrails exceeded
-   Framing: Direct Lake equivalent of refresh — updates metadata pointers to new Delta files
-   No incremental refresh for Direct Lake — framing replaces it
-   Direct Lake on OneLake: reads Delta parquet files directly. Best performance. Source must be in OneLake
-   Direct Lake on SQL endpoints: uses auto-generated SQL endpoint. Supports views but may trigger fallback to DirectQuery
-   Choose OneLake when: data is in lakehouse Delta tables, maximum read performance needed
-   Choose SQL endpoints when: need SQL views, cross-database queries, or data is in warehouse

#### Composite Models & Bridge Tables

-   Mix Import, DirectQuery, and Direct Lake tables in one model
-   Local tables: owned by this model (Import or DirectQuery to external source)
-   Remote tables: chained from another published semantic model (DirectQuery connection)
-   Use case: extend a shared enterprise model with local Import tables for department-specific data
-   Requires Build permission on source semantic model for chaining
-   Bridge tables for many-to-many: dimension → bridge → dimension. Bi-directional cross-filter on bridge relationships

#### CALCULATE & Context

-   CALCULATE: modifies filter context — the most tested DAX function on the exam
-   Context transition: row context → filter context when CALCULATE wraps an iterator
-   CALCULATE + ALL(): removes all filters (total regardless of slicers)
-   CALCULATE + ALLSELECTED(): removes visual-level filters but keeps slicer/page filters
-   CALCULATE + FILTER(): adds row-level filtering, but slower than direct CALCULATE filters
-   CALCULATE + KEEPFILTERS(): intersects new filter with existing (instead of replacing)

#### Time Intelligence

-   REQUIRES: Mark as Date Table (or auto date/time, but disable for perf)
-   Date table must have continuous dates, no gaps, unique date column
-   TOTALYTD / TOTALQTD / TOTALMTD: running totals. Wraps CALCULATE + DATESYTD
-   SAMEPERIODLASTYEAR: same date range from prior year. For YoY comparisons
-   DATEADD(DateColumn, -1, YEAR): shift any period. Flexible alternative
-   PARALLELPERIOD: shifts entire periods (whole months/quarters/years)

#### DAX Patterns

-   VAR/RETURN: evaluated once, improves perf and readability
-   Iterators (X): SUMX, AVERAGEX — table + expression, row context per row
-   Window: INDEX (nth row), OFFSET (relative), WINDOW (range) + ORDERBY/PARTITIONBY
-   Info functions: ISBLANK, HASONEVALUE, ISINSCOPE, SELECTEDVALUE
-   Calc groups: SELECTEDMEASURE(), precedence property (higher = outermost), explicit measures only
-   Field parameters: dynamic column/measure switching in visuals. Created via Tabular Editor or DAX
-   Dynamic format strings: FORMAT expressions on measures for conditional formatting

#### Model Performance

-   Disable Auto date/time (creates hidden tables per date column)
-   Reduce column cardinality — fewer distinct values = better compression
-   Remove unused columns from Import models — every column costs memory
-   Prefer measures over calculated columns (no storage cost)
-   Star schema outperforms snowflake — fewer joins, better scan patterns
-   Default summarisation: set to "Don't summarize" for non-additive columns
-   Data categories (Geography, URL, Image URL, Barcode) — enables map visuals and special rendering

#### Incremental Refresh

-   RangeStart/RangeEnd parameters (exact names, DateTime type!)
-   Query folding strongly recommended — must fold for best performance
-   No equality operator on both parameters simultaneously
-   No incremental refresh for Direct Lake — use framing instead
-   Hybrid tables (real-time + incremental) = Premium/Fabric capacity only

#### External Tools

-   Tabular Editor: OLS config, calc groups, partitions, best practices. THE key enterprise tool
-   DAX Studio: query perf analysis, VertiPaq Analyzer (model compression stats)
-   ALM Toolkit: schema comparison and deployment between environments
-   Best Practice Analyzer (BPA): automated model quality checks

Section 6 / Method of Loci

## The Memory Palace

Walk through themed rooms — each object anchors a concept in spatial memory.

### The Operations Centre

Maintain -Where the analytics solution is governed and monitored

🏢

OneLake & Workspace

One lake per tenant, ADLS Gen2. Roles: Admin > Member > Contributor > Viewer. F-SKU (Fabric), Premium

🔑

Build Permission

Create reports from models, Analyze in Excel, composite models, cross-workspace

⭐

Governance

Endorsed: Promoted → Certified → Master data. Sensitivity Labels propagate downstream. Lineage tracing

🔍

Performance Monitoring

Performance Analyzer: DAX query vs render time. Query Diagnostics for DQ/DL behaviour. Fewer visuals per page

🚀

Deployment & Git

Pipelines: Dev → Test → Prod. Binding rules. Git: PBIP/PBIR. XMLA endpoint for enterprise tools

🛠️

External Tools & Query

Tabular Editor, DAX Studio, ALM Toolkit, BPA. T-SQL, KQL, Visual Query Editor

### The Data Lab

Data Preparation -Where raw becomes refined

🏭

Data Factory

170+ connectors, Copy Activity, pipeline orchestration

🧹

Dataflows Gen2

Power Query Online, query folding indicators (web UI only!)

📊

Data Profiling

Quality (valid/error/empty %), Distribution (distinct/unique), Profile (statistics)

🔗

OneLake Shortcuts

Zero-copy data access across workspaces and clouds

📓

Notebooks

PySpark: broadcast(), withColumn(), cast(). Delta: VACUUM, OPTIMIZE

🔄

Query Folding

5 states: Folding, Not Folding, Might Fold, Opaque, Unknown. Web UI only. CSV never folds

### The Model Workshop

Semantic Models -Where data becomes meaning

⭐

Star Schema

Fact tables (centre) + dimension tables (radiate out). 1-to-many relationships. Single cross-filter direction default. Flatten snowflake into star

🔗

Relationships

1-to-many (default). Bi-directional only for many-to-many bridges. USERELATIONSHIP() for inactive. One active path per table pair

⚡

Storage Modes

Import (fastest, cached) → Direct Lake (reads Delta parquet) → DirectQuery (live, slowest). Composite = mix all three

🔄

Direct Lake

Fallback: Automatic/DirectLakeOnly/DirectQueryOnly. Triggers: RLS, views, guardrails. Framing replaces refresh

🧮

CALCULATE

Modifies filter context — #1 tested concept. Context transition: row→filter. Combine with ALL, FILTER, KEEPFILTERS, time intel

🕐

Time Intelligence

TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, PARALLELPERIOD. REQUIRES marked date table with continuous dates

📐

DAX Patterns

Iterators end in X (SUMX). VAR/RETURN evaluated once. Window funcs need ORDERBY. Calc groups use SELECTEDMEASURE()

🏎️

Performance Tuning

Disable Auto date/time. Reduce cardinality. Remove unused columns. Measures > calc columns. Set "Don't summarize" defaults

### The Security Vault

Security & Governance -Where trust is enforced

🔒

RLS

DAX filter expressions. USERPRINCIPALNAME(). Filters rows only. Dynamic per user

🔐

OLS

Tabular Editor ONLY. Hides entire objects. Cascading: dependent measures vanish

🛡️

CLS

Column-level hiding. Can't protect measures or tables (use OLS for that)

📋

XMLA Endpoint

Enterprise management. Tabular Editor, SSMS. Enable large format for writes

🚀

Deployment Pipelines

Dev → Test → Prod. Data source binding rules. Impact analysis

🔀

Git Integration

PBIP/PBIR format. PR reviews. Notebooks as source files (.py/.sql)

Section 7 / Pattern Recognition

## Pattern Spotter

Decision trees and trigger-answer pairs — see the pattern, know the answer.

Which Data Store?

Which Data Store?  
  ├── Streaming / Events / IoT → Eventhouse (KQL)  ├── Unstructured / Semi-structured → Lakehouse  ├── Need multi-table transactions? → Yes → Warehouse  ├── Need multi-table transactions? → No → Need Spark / ML? → Yes → Lakehouse  ├── Need multi-table transactions? → No → Need Spark / ML? → No → Need T-SQL? → Yes → Warehouse  └── Need multi-table transactions? → No → Need Spark / ML? → No → Need T-SQL? → Either works → Lakehouse (more flexible)

Which Storage Mode?

Which Storage Mode?  
  ├── OneLake Delta tables → Direct Lake  ├── OneLake Delta tables → Need guaranteed no fallback? → DirectLakeOnly setting  ├── External source, needs to be live → DirectQuery  ├── Small-medium, can schedule refresh → Import  └── Mix of sources / needs → Composite Model

Which Security Layer?

Which Security Layer?  
  ├── Which rows users see → RLS  ├── Which columns users see → CLS  ├── Hide entire tables/columns from existence → OLS  ├── Classify data sensitivity → Sensitivity Labels  └── File-level in lakehouse → OneLake RBAC

Which Direct Lake Source?

Which Direct Lake Source?  
  ├── Data in lakehouse Delta tables → Direct Lake on OneLake  ├── Need SQL views or cross-database queries → Direct Lake on SQL endpoints  ├── Data in warehouse tables → Direct Lake on SQL endpoints  ├── Maximum performance, no fallback risk → Direct Lake on OneLake + DirectLakeOnly mode  └── Need to combine with external DirectQuery → Composite Model

Which CALCULATE Pattern?

Which CALCULATE Pattern?  
  ├── Ignore all slicers / show grand total → CALCULATE + ALL(Table)  ├── Ignore visual filters, keep page/slicer filters → CALCULATE + ALLSELECTED()  ├── Remove filter on one column only → CALCULATE + ALLEXCEPT or REMOVEFILTERS  ├── Add a filter AND keep existing filters → CALCULATE + KEEPFILTERS()  ├── Year-to-date running total → CALCULATE + DATESYTD (or TOTALYTD shortcut)  ├── Same period last year comparison → CALCULATE + SAMEPERIODLASTYEAR  └── Use an inactive relationship → CALCULATE + USERELATIONSHIP()

Which External Tool?

Which External Tool?  
  ├── Configure OLS → Tabular Editor  ├── Create calculation groups → Tabular Editor  ├── Analyse DAX query performance → DAX Studio  ├── Compare/deploy models between environments → ALM Toolkit  ├── Check model best practices → Best Practice Analyzer  └── Analyse model storage/compression → VertiPaq Analyzer

## Decision Cards

"zero-copy" or "no data duplication"→OneLake Shortcuts

"schema-on-read"→Lakehouse

"multi-table transactions"→Warehouse

"streaming" or "telemetry" or "IoT"→Eventhouse

"objects completely hidden" or "as if deleted"→OLS (Object-Level Security)

"Tabular Editor required"→OLS configuration

"SELECTEDMEASURE()"→Calculation Groups

"RangeStart / RangeEnd"→Incremental Refresh

"fallback to DirectQuery"→Direct Lake on SQL endpoints

"query folding required"→Incremental Refresh

"single source of truth"→Master data endorsement

"propagate downstream"→Sensitivity Labels

"VACUUM or OPTIMIZE"→Delta Table Maintenance

"broadcast()"→Small DataFrame optimisation in PySpark

"precedence property"→Calculation Groups

"context transition" or "row becomes filter"→CALCULATE inside an iterator

"mark as date table" or "continuous dates"→Time Intelligence requirement

"TOTALYTD" or "SAMEPERIODLASTYEAR"→Time Intelligence (needs marked date table)

"USERELATIONSHIP()"→Activating inactive relationships in CALCULATE

"bi-directional" or "both directions"→Cross-filter direction (avoid unless many-to-many)

"disable auto date/time"→Model performance optimisation

"framing" (not "refresh")→Direct Lake metadata pointer update

"star schema" or "fact and dimension"→Semantic model design best practice

"remote model" or "chained model"→Composite Model with DirectQuery to published model

"bridge table" or "many-to-many relationship"→Bridge table pattern with bi-directional cross-filter

"Direct Lake on SQL endpoint" or "views in Direct Lake"→Direct Lake on SQL endpoints (may fallback)

"extend enterprise model" or "department-specific"→Composite model: chain remote + local Import tables

"KEEPFILTERS()"→Intersect (not replace) filters in CALCULATE

"PBIP or PBIR format"→Git Integration / version control

"impact analysis"→Deployment Pipelines / Lineage

"enable large format even for small models"→XMLA write operations

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![Keith Oak](https://lucidlabs.com.au/team/koak-400.jpg)

Keith Oak

Chief Technology Officer — Lucid Labs

Microsoft Solutions Partner architect specialising in Fabric, Azure Data & AI, and GitHub Enterprise. 18+ years delivering data platforms for Australian businesses — building the systems these exams test every day.

[LinkedIn ↗](https://www.linkedin.com/in/keithoak/)[lucidlabs.com.au ↗](https://lucidlabs.com.au/)Published 29-03-2026