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Databricks · Associate

Databricks Data Analyst Associate

Query, analyse, visualise and share data with Databricks SQL, AI/BI Dashboards and Genie. Lessons, exam-style practice, timed mocks and an AI tutor, mapped to every objective of the official exam guide.

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21lessons
258exam-style questions
4practice exams
11hands-on labs

The exam

Questions
45
Time
90 minutes
Passing score
Not published by Databricks; this course targets ≥ 80% on unseen mocks
Exam fee
USD 200 (plus applicable taxes)
Valid for
2 years (recertify by taking the current live exam)
Delivery
Online proctored or test center

Official exam page · booked with Databricks

Syllabus

Every section of the exam guide

9 sections and 39 objectives, in the official weights. Each lesson is mapped to the objectives it covers.

Section 1 · Understanding of Databricks Data Intelligence Platform11%

What the exam asks

  • Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.
  • Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
  • Describe the role and features of Databricks Marketplace.

Lessons

  1. Platform Components and Databricks Marketplace
  2. Catalog Explorer: Objects, Access, Certification and Lineage
Section 2 · Managing Data8%

What the exam asks

  • Use Unity Catalog to discover, query, and manage certified datasets.
  • Use the Catalog Explorer to tag a data asset and view its lineage.
  • Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.

Lessons

  1. Discover, Certify, Tag and Trace Data Assets
  2. Cleaning Unity Catalog Tables with SQL
Section 3 · Importing Data5%

What the exam asks

  • Explain the approaches for bringing data into Databricks, covering ingestion from S3, data sharing with external systems via Delta Sharing, API-driven data intake, the Auto Loader feature, and Marketplace.
  • Use the Databricks Workspace UI to upload a data file to the platform.

Lessons

  1. Bringing Data into Databricks
Section 4 · Executing queries using Databricks SQL and Databricks SQL Warehouses20%

What the exam asks

  • Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.
  • Explain the role a SQL Warehouse plays in query execution.
  • Querying cross-system analytics by joining data from a Delta table and a federated data source.
  • Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.
  • Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
  • Write queries to combine tables using various join operations (inner, left, right, and so on) with single or multiple keys, as well as set operations like union and union all, including the differences between the joins (inner, left, right, and so on).
  • Perform sorting and filtering operations on a table.
  • Create managed tables and external tables, including creating tables by joining data from multiple sources (e.g., CSV, Parquet, Delta tables) to create unified datasets, including Unity Catalog.
  • Use Delta Lake's time travel to access and query historical data versions.

Lessons

  1. SQL Warehouses and Genie Code (Databricks Assistant)
  2. Aggregations, Sorting and Filtering
  3. Joins, Set Operations and Federated Queries
  4. Managed and External Tables, CTAS and Delta Time Travel
  5. Views, Materialized Views and Streaming Tables
Section 5 · Analyzing Queries15%

What the exam asks

  • Understand the Features, Benefits, and Supported Workloads of Photon.
  • Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.
  • Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.
  • Utilize query history and caching to reduce development time and query latency
  • Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
  • Fix a query to achieve the desired results.

Lessons

  1. Photon, query history and caching
  2. Query Insights, the query profile and liquid clustering
  3. Delta history for auditing, and fixing query results
Section 6 · Working with Dashboards and Visualizations in Databricks16%

What the exam asks

  • Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).
  • Create visualizations in notebooks and the SQL editor.
  • Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.
  • Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.
  • Schedule an automatic dashboard refresh.
  • Configure an alert with a desired threshold and destination.
  • Identify the effective visualization type to communicate insights clearly.

Lessons

  1. Building AI/BI Dashboards: Datasets, Pages, Widgets and Chart Choice
  2. Visualizations in Notebooks and the SQL Editor, and Working with Parameters
  3. Sharing, Embedding, Scheduled Refresh and Alerts
Section 7 · Developing, Sharing, and Maintaining AI/BI Genie spaces12%

What the exam asks

  • Describe the purpose, key features, and components of AI/BI Genie spaces.
  • Create Genie spaces by defining reasonable sample questions and domain-specific instructions, choosing SQL warehouses, curating Unity Catalog datasets (tables, views...), and vetting queries as Trusted Assets.
  • Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.
  • Optimize AI/BI Genie spaces by tracking user questions, response accuracy, and feedback; updating instructions and trusted assets based on stakeholder input; validating accuracy with benchmarks; refreshing Unity Catalog metadata.

Lessons

  1. Genie spaces: purpose, components and setup
  2. Sharing, distributing and improving Genie spaces
Section 8 · Data Modeling with Databricks SQL5%

What the exam asks

  • Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.
  • Understand how industry-standard models align with the Medallion Architecture.

Lessons

  1. Star, snowflake and data vault models in the medallion lakehouse
Section 9 · Securing Data8%

What the exam asks

  • Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
  • Understand how the 3-level namespace(Catalog / Schema / Tables or Volumes) works in the Unity Catalog.
  • Apply best practices for storage and management to ensure data security, including table ownership and PII protection.

Lessons

  1. Unity Catalog roles, sharing settings and the three-level namespace
  2. Ownership, secure storage and PII protection
Study plan

21-Day Standard Plan

The default path for analysts who write SQL every day but haven't used every part of Databricks SQL, AI/BI dashboards and Genie. It covers all 21 lessons and 11 labs in course order, with a section drill at the end of each section and daily flashcards. Until you take the mocks, keep the practice setting that hides the mock-exam questions switched on, so the drills use only questions that aren't in a mock. Querying (n4, 20%), dashboards (n6, 16%) and analyzing queries (n5, 15%) get the most days because together they carry about half the exam. Mock Exam 1 and Mock Exam 2 are on days 20 and 21, and you sit the exam on day 22 or soon after. About 1–2 hours a day.

  1. Day 1Kick-off: the exam and the platform components
  2. Day 2Catalog Explorer: objects, access, certification and lineage
  3. Day 3Discover, certify, tag, trace and clean
  4. Day 4Cleaning lab and the ways data comes in
  5. Day 5Ingestion lab, SQL warehouses and Genie Code
  6. Day 6Aggregations, sorting and filtering
  7. Day 7Joins, set operations and federated queries
  8. … and 14 more days, with mock exams at the end

Read the free study guide for this exam

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Sample questions

Written for practice in the style of the real exam: never questions from the real exam.

Sample question 1

Customer account data lives in a PostgreSQL database that the application team will not allow to be copied. Order history is in a Delta table in Unity Catalog. An analyst needs to join the two in Databricks SQL. What must be set up first?

  1. AAn external table whose LOCATION points at the PostgreSQL database, created with CREATE TABLE ... LOCATION
  2. BA Unity Catalog connection and a foreign catalog, so the tables can be queried with three-level names
  3. CAn OpenSharing (formerly Delta Sharing) share that the PostgreSQL team publishes for Databricks
  4. DA streaming table that reads the PostgreSQL tables with STREAM read_files on a schedule
Show the answer

B. Lakehouse Federation reads external databases in place. A Unity Catalog connection stores the PostgreSQL host and credentials, and a foreign catalog mirrors one database. Its tables can then be queried, and joined with Delta tables, using catalog.schema.table names on a pro or serverless warehouse. Foreign tables are read-only and governed by Unity Catalog privileges.

Sample question 2

A sales VP asks a Genie space, 'Why did EMEA revenue drop last quarter?' She wants Genie to investigate several possible causes, run multiple queries and return a written report with the evidence behind it, rather than a single result table. Which Genie capability is designed for this?

  1. AA trusted asset that runs the approved revenue function with EMEA as its parameter
  2. BBenchmarks, which test the question against several ground-truth SQL answers
  3. CChat mode with a text instruction telling Genie to explain its reasoning at length
  4. DAgent mode (formerly Research agent), which plans and runs a multi-step analysis
Show the answer

D. Agent mode, formerly called Research agent, handles complex, exploratory questions. It creates and refines a research plan, runs several SQL queries, learns from each result and returns a report with citations, visualizations and supporting tables. Chat mode answers a structured question with one query, trusted assets return vetted logic, and benchmarks only measure accuracy.

Questions

About this exam

What is the passing score?

Databricks doesn't publish it and says passing scores are set statistically and may change. This course's readiness target is ≥ 80% on both mock exams on the first attempt, with no section below 70% across the two.

How many questions come from each section?

The exam page gives weights only: 11% platform, 8% managing data, 5% importing data, 20% querying, 15% analyzing queries, 16% dashboards, 12% Genie, 5% data modeling and 8% securing data. On 45 scored items that's about 5, 4, 2, 9, 7, 7, 5, 2 and 4 questions, which is also how the course's mock exams are built. Individual exam forms vary.

Are there multiple-select questions?

Yes, probably a few: the exam page and FAQ say multiple choice, but sample question 10 in the exam guide asks you to choose two. About one question in ten in this course is a *(Choose two.)* item, so you practise them. Read every stem for the instruction and judge each option on its own.

The exam guide says Databricks Assistant, Genie spaces and Delta Sharing. Which names will I see?

Possibly either. The guide was written in October 2025, before Databricks Assistant became Genie Code (March 2026), Delta Sharing became OpenSharing (June 2026) and Genie spaces became Genie Agents (July 2026). The lessons teach both names, and the Renamed features page maps every one.

Are there unscored questions?

Possibly. The guide says exams may include unscored items to gather statistics. They aren't identified and don't affect your score, and extra time is built in for them. Treat every question as scored.

How much does it cost, and what's the retake policy?

USD 200 plus applicable taxes. Per the certification FAQ you can register again and retake after 14 days; each attempt costs the full fee and there are no free retake vouchers.

How long is the certification valid?

Two years. To recertify you take the full exam that is live at that time, which may follow a newer exam guide.

Can I take it online?

Yes, online proctored or at a test center. You register on Webassessor and Kryterion delivers the exam. Online, you need a webcam, a microphone, a passing system check, a valid photo ID and a private room.

Can I do the labs on Databricks Free Edition?

Yes. Free Edition has one 2X-Small serverless SQL warehouse and Unity Catalog, which is enough for every lab. A few steps can't run there because Free Edition has no external locations on your own storage, for example creating an external table on an S3 bucket; the labs say so and show the statement instead.

How much Python do I need?

Very little. Almost everything is SQL and the UI. Expect to recognize display(df), dbutils.data.summarize, an Auto Loader cloudFiles read and notebook widgets, not to write Python programs.

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