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No prior Databricks or data analytics experience. You'll build foundational SQL and analytics skills from scratch over 3 weeks.
Day 1–2Learn data analytics fundamentals: data lakes vs data warehouses, the Lakehouse concept, Medallion Architecture (Bronze/Silver/Gold), and where analysts fit in the data platform
Day 3–4Databricks SQL basics: workspaces, SQL warehouses (Classic vs Pro vs Serverless), the Query Editor, Data Explorer, and Unity Catalog overview. Sign up for Databricks Community Edition
Day 5–7SQL fundamentals: SELECT, FROM, WHERE, JOIN types, GROUP BY, HAVING, ORDER BY, LIMIT. Practice writing queries against sample tables in Databricks SQL
Day 8–9Intermediate SQL: subqueries, CTEs (WITH clause), CASE expressions, COALESCE, CAST, date functions, string functions. Understand SQL execution order
Day 10–11Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, SUM/AVG OVER (PARTITION BY). Practice running totals, rankings, and period-over-period comparisons
Day 12–13Data management: managed vs external tables, views vs temporary views, Delta Lake basics (ACID, time travel, DESCRIBE HISTORY), Unity Catalog three-level namespace
Day 14–15Dashboards and visualization: create dashboards, add visualizations (bar, line, scatter, pivot), use markdown text boxes, configure filters and parameters
Day 16–17Alerts and analytics applications: set up SQL alerts with thresholds, schedule query refreshes, understand Genie Spaces, Partner Connect, and BI tool integration
Day 18–19Data governance: permissions (GRANT/REVOKE), PII handling, data quality, data enrichment, last-mile ETL. Review the Query Profile for performance tuning
Day 20Practice questions across all 5 domains, review explanations carefully
Day 21Take a full mock exam. Review all wrong answers. Retake if below 75%
Exam Overview
Format
45 questions, 90 minutes. Multiple choice (single select and multiple select). SQL-heavy — questions test Databricks SQL syntax, dashboard creation, data visualization, and analytical problem-solving.
Scoring
Pass/fail based on percentage score. Passing: 70%. No penalty for wrong answers — always guess if unsure.
Domains & Weights
- Databricks SQL22%
- Data Management20%
- SQL Programming29%
- Data Visualization and Dashboarding14%
- Analytics Applications15%
Registration
$200 USD. Available through Kryterion testing centers or online proctored. Schedule at databricks.com/certification.
Topic Priority Table
Not all topics are tested equally. Focus your study time on Tier 1 first, then Tier 2. Tier 3 topics rarely appear — just recognize what they do.
Domain 122% of exam
Databricks SQL
A major domain at ~22%. Tests your understanding of the Databricks SQL platform: SQL warehouses, the Query Editor, Data Explorer, Query History, Query Profile, Delta Lake basics, and how Databricks SQL fits within the Lakehouse Platform. Expect conceptual questions about platform features and practical questions about navigating the workspace.
Key Topics
SQL WarehousesQuery EditorData ExplorerQuery HistoryQuery ProfileDelta LakeUnity Catalog
Must-Know Concepts
- SQL warehouse types: Classic (manual config), Pro (governance features), Serverless (instant startup, auto-scale, no cluster management)
- Data Explorer: browse table metadata, preview data, view column details, manage permissions, transfer ownership — all without writing SQL
- Query History: review past queries with execution metrics, query plans, and timing — used for troubleshooting and performance analysis
- Query Profile: visual execution plan showing operators, data flow, memory usage — identifies bottlenecks in slow queries
- Delta Lake fundamentals: ACID transactions, time travel (VERSION AS OF, TIMESTAMP AS OF), DESCRIBE HISTORY, transaction logs
- Managed vs external tables: dropping managed deletes data; dropping external removes only metadata
- Views persist in the metastore across sessions; temporary views exist only for the current session
- Unity Catalog three-level namespace: catalog.schema.table — know how to use fully qualified table names
Common Exam Traps
Serverless SQL warehouses eliminate startup latency — if a question mentions slow startup or cost optimization for intermittent workloads, choose Serverless
Data Explorer can transfer table ownership by clicking the Owner field — no SQL needed for this operation
Query History is read-only diagnostic — it cannot schedule queries or automate workflows
Delta Lake stores data files + transaction logs + metadata — all three contribute to performance, not data files alone
Domain 220% of exam
Data Management
A significant domain at ~20%. Tests your understanding of the Lakehouse architecture, Medallion Architecture layers, Delta Lake features, Unity Catalog governance, data ingestion concepts, data quality, PII handling, and data enrichment. Expect both conceptual and practical questions about managing data within the platform.
Key Topics
Delta LakeUnity CatalogMedallion ArchitectureData GovernanceData Enrichment
Must-Know Concepts
- Medallion Architecture: Bronze (raw, append-only), Silver (cleaned, validated, deduplicated), Gold (business-ready aggregates for analysts)
- Delta Lake provides ACID transactions, time travel, schema enforcement, and audit history — unlike traditional data lakes with Parquet files
- Unity Catalog three-level namespace: catalog.schema.table — all data access goes through this hierarchy
- GRANT/REVOKE syntax: GRANT SELECT ON TABLE catalog.schema.table TO group_name — also need USAGE on catalog and schema
- Managed tables: data stored in Unity Catalog-managed storage; DROP TABLE deletes data. External tables: data in customer-managed storage; DROP TABLE removes metadata only
- PII compliance: consider the jurisdiction where data was collected, local regulations, AND organization-specific governance — not just one factor
- Data enrichment (augmentation): enhancing existing datasets by adding external information or derived attributes
- Unity Catalog tags: use ALTER TABLE SET TAGS ('system.Certified' = 'true') to mark tables as production-ready certified assets
Common Exam Traps
Dropping a managed table permanently deletes data files. Dropping an external table preserves files — the exam tests this distinction repeatedly
PII handling requires considering data source jurisdiction, local regulations, AND internal governance standards — not just one of these
Data enrichment/augmentation is about adding external data to enhance analysis, not about data warehousing or governance
The Medallion Architecture's Gold layer is where analysts spend most of their time — Bronze and Silver are for engineers
Domain 329% of exam
SQL Programming
The largest domain at ~29%. Tests your SQL fluency across the full range: basic queries (SELECT, JOIN, GROUP BY), intermediate patterns (CTEs, CASE, subqueries), advanced features (window functions, PIVOT/UNPIVOT, array operations), and Databricks-specific extensions (QUALIFY, higher-order functions). Expect syntax-heavy questions that require you to choose the correct SQL statement.
Key Topics
Spark SQLWindow FunctionsCTEsPIVOT/UNPIVOTArray FunctionsDate Functions
Must-Know Concepts
- SQL execution order: FROM -> WHERE -> GROUP BY -> HAVING -> SELECT -> ORDER BY -> LIMIT
- JOIN types: INNER, LEFT, RIGHT, FULL OUTER, CROSS, SEMI, ANTI — know when to use each
- Window functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), SUM/AVG OVER (PARTITION BY ... ORDER BY ...)
- QUALIFY clause: Databricks shortcut to filter on window function results without needing a subquery (e.g., QUALIFY ROW_NUMBER() OVER (...) = 1)
- CTEs: WITH clause for defining reusable named result sets — improves readability over nested subqueries
- GROUP BY with aggregate functions: COUNT, SUM, AVG, MIN, MAX — produces one row per group
- INSERT INTO appends rows (preserves existing data). INSERT OVERWRITE replaces all existing data
- PIVOT: transforms rows into columns for cross-tabular analysis. UNPIVOT: transforms columns into rows
- Array operations: EXPLODE (flatten array to rows), ARRAY_CONTAINS / ANY() for searching arrays
- CASE WHEN for conditional logic in SELECT, COALESCE for null handling, CAST for type conversion
Common Exam Traps
Window functions cannot be used in WHERE clauses — use QUALIFY or wrap in a subquery/CTE
GROUP BY reduces rows (one per group). Window functions (OVER) preserve all original rows. Know which to use for each scenario
INSERT INTO adds rows to existing data. INSERT OVERWRITE replaces existing data. Do not confuse them
SQL execution order puts SELECT after GROUP BY — you cannot reference a column alias defined in SELECT within the WHERE clause
SUM() OVER (PARTITION BY ... ORDER BY ...) produces a running total, not a per-group total. For per-group totals, use GROUP BY
Domain 414% of exam
Data Visualization and Dashboarding
This domain covers ~14%. Tests your ability to create effective dashboards in Databricks SQL: adding visualizations, choosing appropriate chart types, using markdown text boxes for organization, configuring filters and parameters, scheduling automatic refreshes, and sharing dashboards with stakeholders.
Key Topics
Databricks DashboardsVisualization TypesMarkdown TextFiltersParametersScheduled Refresh
Must-Know Concepts
- Dashboards combine multiple query-based visualizations, markdown text, and filter widgets on a single page
- Visualization types: bar charts, line charts, scatter plots, pie charts, tables, counters, pivot tables, maps — choose based on data type and audience
- Markdown text boxes add section headers, labels, and descriptions to organize dashboards visually
- Filter widgets let dashboard viewers dynamically slice data without modifying queries
- Parameterized queries: use {{ parameter_name }} syntax to create dynamic queries that accept user input
- Dashboard scheduling: built-in refresh schedules (hourly, daily, custom cron) ensure data freshness without manual intervention
- Sharing: dashboards can be shared with users/groups via permissions — viewers see data based on their own Unity Catalog access
Common Exam Traps
Dashboard refresh frequency is bounded by the underlying data pipeline — a one-minute streaming batch means the dashboard cannot show data fresher than one minute
Markdown text boxes are the correct way to add section headers to dashboards — not query result columns or SQL comments
Dashboards display data based on the viewer's permissions — sharing a dashboard does not grant access to the underlying tables
Scheduled dashboard refreshes require an active SQL warehouse — if the warehouse is stopped, the refresh will start it (which may cause latency with Classic warehouses)
Domain 515% of exam
Analytics Applications
This domain covers ~15%. Tests your understanding of how Databricks SQL fits within the broader analytics ecosystem: BI tool integration (Tableau, Power BI), Genie Spaces for natural language queries, Partner Connect for tool provisioning, Databricks Marketplace for external data, alert-based monitoring, and analytical use cases appropriate for the platform.
Key Topics
BI Tool IntegrationGenie SpacesPartner ConnectDatabricks MarketplaceSQL AlertsAnalytical Workflows
Must-Know Concepts
- Databricks SQL is complementary to BI tools (Tableau, Power BI) — it's for ad-hoc analytics and rapid prototyping, not a full BI replacement
- Partner Connect: automatically provisions SQL warehouses and establishes connections to third-party tools with minimal manual configuration
- Genie Spaces: AI-powered natural language interface for data exploration — translates plain English questions into SQL queries
- Databricks Marketplace: discover and access third-party datasets via Delta Sharing — live access without data replication
- SQL alerts: automated threshold monitoring with scheduled query evaluation and notifications (email, Slack, webhook)
- Alert statuses: OK (threshold not exceeded), TRIGGERED (threshold exceeded), UNKNOWN (not yet evaluated or query failed)
- Databricks SQL is ideal for: scheduled alerts, ad-hoc queries, dashboards, exploratory analytics — NOT for ML model training or complex ETL
Common Exam Traps
Databricks SQL complements BI tools — it doesn't replace Tableau or Power BI. If a question asks about 'positioning,' choose 'complementary for ad-hoc analytics'
Partner Connect is the most efficient way to set up third-party integrations — manual configuration is the wrong answer
Genie Spaces respects Unity Catalog permissions — natural language queries don't bypass data access controls
Alert status TRIGGERED means the condition was met, not that the alert is turned on. UNKNOWN means it hasn't been evaluated yet or the query failed
Key Databricks SQL Concepts Compared
These pairs appear on nearly every exam. Learn the difference and you'll avoid the most common traps.
Top Mistakes to Avoid
Confusing managed tables (DROP deletes data) with external tables (DROP removes metadata only) — the most tested distinction on the exam
Using window functions directly in WHERE clauses — they must be in a subquery, CTE, or use the QUALIFY clause
Confusing GROUP BY (reduces rows to one per group) with window functions (preserves all rows and adds computed columns)
Thinking INSERT INTO replaces existing data — it appends. INSERT OVERWRITE replaces. Know the difference
Assuming GRANT SELECT on a table is sufficient — users also need USAGE permissions on the catalog and schema
Using SUM() OVER (PARTITION BY ... ORDER BY ...) expecting per-group totals — this produces running totals. Remove ORDER BY for per-group totals
Confusing alert status TRIGGERED (condition met) with ACTIVATED (alert enabled) — the exam uses precise terminology
Thinking Databricks SQL replaces Tableau/Power BI — it complements them for ad-hoc analytics and prototyping
Exam-Ready Checklist
Can explain all 5 exam domains and their relative weights (SQL Programming is the largest at 29%)
Know Delta Lake fundamentals: ACID transactions, time travel syntax, DESCRIBE HISTORY, managed vs external tables
Can write SELECT queries with JOINs, GROUP BY, HAVING, and ORDER BY from memory
Comfortable with window functions: ROW_NUMBER, RANK, LAG, LEAD, running totals, and the QUALIFY clause
Understand CTEs (WITH clause), subqueries, CASE WHEN, COALESCE, and CAST
Know Databricks SQL platform features: SQL warehouses, Data Explorer, Query History, Query Profile
Can create and configure dashboards: visualizations, markdown text, filters, parameters, scheduled refreshes
Understand SQL alerts: threshold setup, statuses (OK/TRIGGERED/UNKNOWN), notification targets
Know Unity Catalog namespace (catalog.schema.table) and GRANT/REVOKE syntax
Understand BI ecosystem positioning: Partner Connect, Genie Spaces, Databricks Marketplace, BI tool integration
Scored 80%+ on at least two full practice exams
Reviewed all incorrect answers and understand why the right answer is right
Can complete the exam within time: average 2 minutes per question
Recommended Resources
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