Databricks Certified Data Analyst Associate Roadmap

Build the governed mental model before writing dashboard SQL.

• Map Databricks SQL, Delta Lake, Unity Catalog, Lakeflow Jobs, Mosaic AI, the Data Intelligence Engine, and Marketplace to their responsibilities.
• Use Catalog Explorer to inspect catalogs, schemas, managed and external tables, views, certification, tags, ownership, and lineage.
• Profile invalid and missing values and apply documented cleaning rules rather than automatic coercion.
• Compare UI upload, cloud object storage intake, Auto Loader, API-driven intake, Delta Sharing, Marketplace, and federation from source, cadence, volume, ownership, and copying requirements.
• Practice the three-level Unity Catalog namespace and separate durable ownership from day-to-day SELECT access.

Master query grain, compute, and reliable table patterns.

• Use the SQL editor or notebooks with Databricks Assistant for generation, explanation, and debugging while independently validating output.
• Explain why a SQL warehouse supplies compute and how sizing, auto-stop, serverless availability, permissions, and concurrency affect delivery.
• Write aggregates, exact and approximate distinct counts, filters, sorting, inner and outer joins, composite-key joins, UNION, and UNION ALL.
• Create managed and external tables from CSV, Parquet, and Delta sources and expose approved views.
• Choose streaming tables for incremental event processing and materialized views for refreshed stored query results.
• Use Delta time travel only while the required transaction log and data files remain retained.

Diagnose wrong or slow SQL with measured evidence.

• Start with metric definition and row grain; identify one-to-many joins that multiply measures.
• Use query history to compare status, duration, warehouse, user, and repeated executions.
• Use query profile and performance insights to inspect operators, scans, rows, joins, and shuffles.
• Understand Photon as an execution engine, not a correction for invalid logic.
• Compare result caching behavior using equivalent query and data conditions.
• Apply liquid clustering only when large-table filter patterns justify selected keys, then rerun correctness and performance controls.
• Validate changes against known totals, edge cases, historical versions, and stable fixtures.

Turn correct queries into understandable, fresh, and authorized decisions.

• Build multi-page AI/BI dashboards with several datasets, visualizations, text, images, filters, and typed parameters.
• Select lines for ordered trends, bars for discrete comparisons, tables for precise detail, and scatter plots for relationships.
• Test parameter defaults, mappings, empty states, and invalid inputs.
• Compare run-as-viewer and run-as-owner authorization implications with separate personas.
• Configure refresh schedules, subscribers, threshold alerts, destinations, freshness monitoring, and response runbooks.
• Use supported sharing and embedding controls; never put an owner's credential in client code.

Connect semantic quality, conversational analytics, and least privilege.

• Apply star, snowflake, and data-vault concepts and explain how gold dimensional models can follow bronze and silver medallion layers.
• Curate narrow, well-described Unity Catalog tables and views for an AI/BI Genie space.
• Write domain instructions, sample questions, synonyms, and explicit out-of-scope behavior.
• Inspect generated SQL and add only verified recurring patterns as trusted assets.
• Benchmark direct, paraphrased, ambiguous, and unauthorized questions; use feedback to update metadata and instructions.
• Protect PII with restricted base tables, approved views or semantic objects, masks or row controls where required, durable ownership, and least privilege.
• Complete all three projects, review 40 unique cards, and answer the bank using the exact 6/4/2/10/8/8/6/2/4 allocation.