ExamFinch.com My Study

Cheat sheet

Databricks Data Analyst Associate Cheat Sheet

Practice this exam

Data Intelligence Platform

  • 11% of exam
  • Unity Catalog Catalog Explorer Marketplace Lineage Graph

Managing Data

  • 8% of exam
  • Certified Tags Data Cleaning Lineage Check COALESCE/NULLIF

Importing Data

  • 5% of exam
  • Auto Loader COPY INTO Delta Sharing Marketplace Access

Executing Queries

  • 20% of exam
  • SQL Warehouses Tables vs Views Joins + PIVOT Time Travel

Analyzing Queries

  • 15% of exam
  • Query History Query Profile Caching Liquid Clustering

Dashboards + Visualizations

  • 16% of exam
  • Parameters Scheduled Refresh Alerts Embedding

AI/BI Genie Spaces

  • 12% of exam
  • Instructions Trusted Assets Sample Questions Benchmarks

Data Modeling

  • 5% of exam
  • Star Schema Snowflake Schema Data Vault Medallion

Securing Data

  • 8% of exam
  • RBAC Row Filters Column Masks Namespace

Quick Facts

  • Exam Databricks DA
  • Credential Data Analyst Associate
  • Questions 45 scored
  • Time 90 min
  • Pass 70%
  • Fee $200
  • Validity 2 years
  • Blueprint Oct 30 2025

Unity Catalog Namespace Order PL

  • Always catalog, then schema, then table
  • Catalog: top-level container Schema: groups tables Table: the actual object

Unity Catalog Hierarchy PL

  • Metastore Top-level UC container
  • Catalog Groups schemas together
  • Schema Groups tables and views
  • Table Structured data object
  • View Saved query logic
  • Volume Non-tabular file storage
  • Function Registered SQL or Python logic
  • Model Registered ML model

Discovery + Governance Tools PL

  • Catalog Explorer Browse schemas, tables, permissions
  • Marketplace Discover shared data listings
  • Lineage graph Trace upstream/downstream dependencies
  • Data Intelligence Platform Unified lakehouse plus AI
  • System tables Built-in operational metadata tables
  • Search Find governed data assets

Managing Certified Data MA

  • Certified tag Steward-reviewed trusted dataset
  • Custom tag Searchable metadata label
  • COALESCE Replace null with value
  • NULLIF Return null if equal
  • REGEXP_REPLACE Pattern-based string cleanup
  • Lineage check Verify upstream source table

Delta Sharing vs Marketplace IM

Delta Sharing

  • Direct point-to-point share
  • Works cross-platform

Marketplace

  • Public or private listings
  • Browse and subscribe
  • Private link vs public catalog

Data Ingestion Methods IM

  • UI upload Manual file import
  • Auto Loader Incremental cloud-file ingestion
  • COPY INTO SQL-based file loading
  • Delta Sharing Cross-platform secure data share
  • API intake Programmatic data ingestion
  • Marketplace access Subscribe to shared listings

Warehouse Startup Speed Order EX

  • Serverless seconds, Pro minutes, Classic slowest
  • Serverless: starts in seconds Pro: about four minutes Classic: entry-level only

Materialized View vs Streaming Table EX

Materialized View

  • Batch semantics
  • Recomputes or refreshes incrementally
  • Best for Silver/Gold layer

Streaming Table

  • Streaming semantics
  • Row seen exactly once
  • Best for Bronze ingest
  • Correctness vs continuous ingest

Which View Type to Use EX

  • Need governed base table → Managed table
  • Data lives outside UC → External table
  • Simple saved query logic → View
  • Expensive BI-ready aggregation → Materialized view ( Batch refresh )
  • Continuous append-only ingest → Streaming table
  • Row or column security → Dynamic view

SQL Warehouse Types EX

  • Serverless Starts in seconds
  • Pro ~4-minute startup, no IWM
  • Classic Entry-level performance only
  • Photon Vectorized engine, all tiers
  • Predictive I/O Smart file and row skipping
  • IWM AI-managed serverless scaling
  • Auto-stop Idle warehouse shuts down

Managed vs External Table EX

Managed Table

  • UC owns the files
  • DROP deletes underlying data
  • Simplifies governance

External Table

  • You own storage path
  • DROP keeps underlying files
  • Needs external location
  • UC-owned vs self-owned

Which SQL Warehouse to Pick EX

  • Bursty unpredictable analyst load → Serverless warehouse
  • Steady workload, no IWM need → Pro warehouse
  • Basic entry-level testing only → Classic warehouse
  • Need fastest possible startup → Serverless warehouse ( Seconds not minutes )

Tables and Views EX

  • Managed table UC owns underlying files
  • External table You own storage location
  • View Recomputed every query
  • Materialized view Precomputed, batch-refreshed result
  • Streaming table Append-only incremental ingest
  • Dynamic view Row/column security logic

Serverless vs Pro Warehouse EX

Serverless

  • Starts in seconds
  • AI-based IWM scaling
  • Lower cost, bursty loads

Pro

  • ~4-minute startup
  • No IWM scaling
  • Better for steady load
  • Instant vs provisioned compute

SQL Query Techniques EX

  • INNER JOIN Matching rows only
  • LEFT JOIN All left, matched right
  • UNION ALL Combine rows, keep duplicates
  • PIVOT Rotate values into columns
  • VERSION AS OF Delta time travel by version
  • FILTER Higher-order array filter
  • TRANSFORM Higher-order array mapper

Query History vs Query Profile AN

Query History

  • Lists past query runs
  • Shows duration and status

Query Profile

  • Shows one execution plan
  • Stage-level timing detail
  • List view vs deep-dive

Diagnosing a Slow Query AN

  • Query already ran once → Check Query History
  • Need stage-level timings → Open Query Profile
  • Repeated identical query text → Check result cache
  • Filter on one huge column → Add Liquid Clustering
  • Need Delta write history → Run DESCRIBE HISTORY
  • Row count larger than expected → Recheck join keys

Query Performance + Tuning AN

  • Query History List of past runs
  • Query Profile Stage-level execution timings
  • Result cache Reuses identical prior query
  • Disk cache Caches remote file reads
  • Liquid Clustering Flexible, incremental data layout
  • DESCRIBE HISTORY Delta table audit log
  • Spill to disk Memory overflow, indicates slowness

Notebook Chart vs AI/BI Dashboard DA

Notebook Chart

  • One-off exploration
  • Lives inside one cell

AI/BI Dashboard

  • Recurring stakeholder view
  • Shareable, scheduled, alerting
  • Explore vs publish

AI/BI Dashboard Features DA

  • Parameter Reusable filter-like input
  • Scheduled refresh Automatic periodic data update
  • Alert Notify on threshold breach
  • Shareable link Controlled external audience access
  • Embedding Dashboard inside external app
  • Multi-page layout Organize related visuals together

Genie Quality Improvement Loop GE

  • Curate, instruct, sample, benchmark, improve
  • Curate trusted assets first Add clear instructions Provide sample questions Track benchmarks and feedback

Improving a Genie Space GE

  • Business jargon misunderstood → Add instructions
  • Users unsure what to ask → Add sample questions
  • Answers cite wrong tables → Curate trusted assets
  • Need ongoing quality tracking → Review benchmarks, feedback

Genie Space Setup GE

  • Instructions Guide Genie's business terms
  • Sample questions Show users example prompts
  • Trusted assets Verified queries Genie prioritizes
  • Benchmarks Track answer quality over time
  • Feedback Users flag good or bad
  • Curated datasets Scoped tables Genie can query

Medallion Architecture Layers DA

  • Bronze raw, Silver clean, Gold curated
  • Bronze: raw ingest Silver: cleaned and joined Gold: BI-ready curated

Star vs Snowflake Schema DA

Star Schema

  • Denormalized dimensions
  • Fewer joins needed
  • Faster BI reads

Snowflake Schema

  • Normalized dimensions
  • More joins needed
  • Less data redundancy
  • Simple vs normalized

Choosing a Data Model Pattern DA

  • Simple BI star reporting → Star schema
  • Normalize shared dimension data → Snowflake schema
  • Need full historical auditability → Data vault
  • Raw to trusted layering → Medallion architecture

Data Modeling Patterns DA

  • Star schema Denormalized facts and dimensions
  • Snowflake schema Normalized, more joins
  • Data vault Hubs, links, satellites model
  • Fact table Measures and foreign keys
  • Dimension table Descriptive business attributes

RBAC vs Row/Column Masking SE

RBAC (GRANT)

  • Object-level access control
  • All-or-nothing per object

Row Filter / Column Mask

  • Row-level visibility rules
  • Column value obscuring
  • Access vs visible content

Unity Catalog Security SE

  • Three-level namespace Catalog.schema.table addressing
  • RBAC GRANT/REVOKE object privileges
  • Row filter Restrict visible rows
  • Column mask Obscure sensitive column values
  • Ownership Controls who can grant
  • PII protection Mask or restrict sensitive data

Common Traps

Managed Table vs External Drop

  • Managed drop deletes files ≠ External drop keeps files

View vs Materialized View

  • View recomputes every run ≠ MV stores precomputed result

Cached Result vs Fresh Profile

  • Cached run skips profiling ≠ Profile needs fresh execution

Certified Tag vs Data Quality

  • Certified means steward-reviewed ≠ Not a quality guarantee

Dashboard vs Genie Space

  • Dashboard shows fixed view ≠ Genie answers ad hoc

Photon vs Serverless Warehouse

  • Photon is query engine ≠ Serverless is compute type

Tag vs Comment Metadata

  • Tags are searchable metadata ≠ Comments are free text

Last Minute

  • 1. 45 scored questions in 90 minutes
  • 2. Pass at 70% or higher
  • 3. SQL execution weighted heaviest at 20%
  • 4. Dashboards, queries, Genie exceed half weight
  • 5. Managed table drop deletes data
  • 6. External table drop keeps files
  • 7. Materialized views are batch, precomputed
  • 8. Streaming tables are append-only ingest
  • 9. Serverless warehouses start in seconds
  • 10. Genie needs curated trusted assets
  • 11. Row filters and column masks differ
  • 12. Namespace order is catalog.schema.table

Databricks Certified Data Analyst Associate

  • Practice Questions 200 questions Study Guide 38 topics Cheat Sheet 76 facts Flashcards 50 cards Shorts 2 short s Videos 1 video Blogs 1 blog Free CE Podcast