Databricks Certified Data Analyst Associate Practice Exam
Practice across all nine exam domains — from Databricks SQL querying and Unity Catalog data management to dashboards, AI/BI Genie spaces, data modeling, and security. Get immediate feedback in Learn mode and a full 90-minute simulation in Exam mode. Start with a 24-hour free trial.
Start 24-hour free trial →Exam at a glance
- Exam
- Databricks Certified Data Analyst Associate
- Format
- Multiple choice, proctored (online or test center)
- Scored questions
- 45 (additional unscored items may appear)
- Time limit
- 90 minutes
- Registration fee
- $200 USD, plus applicable local taxes
- Prerequisites
- None; related training highly recommended
- Recommended experience
- 6+ months of hands-on data analysis on Databricks
- Passing standard
- Databricks does not publish a fixed numeric passing score
- Validity
- 2 years; recertify by taking the current exam
- Languages
- English
- Blueprint edition
- Data Analyst Associate Exam Guide (Oct 2025 edition)
Source: Databricks — Certified Data Analyst Associate · Exam Guide PDF
About this certification
The Data Analyst Associate is Databricks’ entry-level analytics credential. It validates that you can perform everyday data-analysis work on the Databricks Data Intelligence Platform using Databricks SQL: discovering and managing data in Unity Catalog, importing data through methods like the UI, Auto Loader, Delta Sharing, and the Marketplace, writing and optimizing SQL queries, building dashboards and visualizations, and developing AI/BI Genie spaces. It is aimed at analysts who work with data in SQL rather than at engineers building pipelines or data scientists training models.
The exam is practical and scenario-based: questions describe a realistic analyst task — choosing the right SQL statement, fixing a query, configuring a dashboard parameter, setting a Unity Catalog permission — and ask for the correct Databricks approach. All querying adheres to ANSI SQL standards, so SQL fluency is the backbone of the exam. For foundational reading on analytics with Databricks SQL, see the Data Analytics Learning Hub guide.
Exam domains and weights
The exam is divided into nine domains. Weights are taken directly from the official Databricks exam page; approximate question counts are derived from the 45 scored questions and rounded.
The platform fundamentals — the Lakehouse, Databricks SQL, SQL warehouses, and where the analyst's tools sit.
Discovering, querying, cleaning, and managing data and certified datasets with Unity Catalog, including managed vs. external tables.
Ingesting data via the UI, S3, Delta Sharing, APIs, Auto Loader, and the Marketplace.
The largest domain. Writing and running queries, creating views, aggregations, and joins on Databricks SQL warehouses.
Filtering, sorting, and analyzing queries using auditing, history, query logs, and Liquid clustering features.
Building visualizations and dashboards, including query and dashboard parameters and sharing.
Developing, sharing, and maintaining AI/BI Genie spaces for natural-language analytics.
Modeling data for analytics — table relationships, schema design, and analytics-friendly structures in Databricks SQL.
Best practices for data storage and management — Unity Catalog permissions, governance, and secure sharing.
Who this exam is for
This credential fits data analysts, business analysts, BI developers, and SQL-focused professionals who work with data on Databricks. There are no formal prerequisites, so anyone can register; in practice Databricks recommends around six months of hands-on experience with Databricks SQL tools. Solid SQL fundamentals (ANSI standard), familiarity with Unity Catalog, and comfort building dashboards are effectively expected.
If your work leans toward building data pipelines rather than analyzing data, the Data Engineer Associate is a closer fit; if you are moving into machine learning, the ML Associate is the relevant next step. For role-by-role salary ranges and career paths, see the Career Hub — Data Analyst role guide.
What this practice exam delivers
Learn mode
Answer one question at a time with the explanation revealed immediately — ideal for the query-execution domain, where reading SQL carefully and picking the correct statement is the whole point.
Exam mode
45 questions against a 90-minute timer — the real exam format. Build the pacing the scenario-based, SQL-heavy questions demand before test day.
Source-linked explanations
Every answer cites the Databricks documentation it derives from — Databricks SQL, Unity Catalog, dashboards, AI/BI Genie — so you can verify the reasoning and dig deeper.
Score by exam domain
Results break down across all nine domains, so practice tells you exactly which area — querying, dashboards, Genie, security — to study next.
Sample practice questions
Ten free questions spanning the nine exam domains, each with a full explanation of why the other answers are wrong. The complete bank is available with the 24-hour trial.
An analyst needs to quickly create SQL queries and dashboards on serverless compute within the Databricks platform. Which service meets all of these requirements?
- Databricks Machine Learning
- Databricks Notebooks
- Databricks SQL
- Delta Lake
Show answer & explanation
Correct: C — Databricks SQL. Databricks SQL provides the SQL editor, serverless SQL warehouses, and dashboarding the analyst needs in one service — the home base for the Data Analyst role.
Why not the others: Databricks Machine Learning (A) targets ML workflows, not SQL analytics; Notebooks (B) are general-purpose authoring, not the serverless SQL warehouse + dashboard combination; Delta Lake (D) is the storage format underneath, not the analytics service. Databricks SQL ties the requirements together.
Source: Databricks — Databricks SQL →An analyst wants to remove a managed table and all of its underlying data files from a database, while leaving the other tables intact. Which command does this without error?
- DELETE TABLE database_name.table_name;
- DROP TABLE database_name.table_name;
- DROP DATABASE database_name;
- TRUNCATE database_name;
Show answer & explanation
Correct: B — DROP TABLE. For a managed table, DROP TABLE removes both the metadata and the underlying data files, leaving the rest of the database untouched.
Why not the others: DELETE TABLE (A) is not valid SQL (DELETE operates on rows, not table objects); DROP DATABASE (C) would remove every table, not just one; TRUNCATE on a database (D) is invalid. Only DROP TABLE meets the requirement cleanly.
An analyst needs to incrementally and efficiently ingest new data files as they arrive in cloud storage, without reprocessing files already loaded. Which Databricks capability is designed for this?
- A one-time manual CSV upload through the UI
- Auto Loader
- Copying files into a notebook cell
- Exporting to a spreadsheet
Show answer & explanation
Correct: B — Auto Loader. Auto Loader incrementally processes new files as they land in cloud storage, tracking what has already been ingested so files are not reprocessed — the right tool for ongoing ingestion.
Why not the others: a one-time UI upload (A) does not handle continuously arriving files; pasting into a notebook (C) is manual and not scalable; exporting to a spreadsheet (D) is the opposite of ingestion. Auto Loader handles incremental intake.
Source: Databricks — Auto Loader →An analyst wants to save a reusable, named query definition that other queries and dashboards can select from, without physically copying the data. Which object should they create?
- A view
- A second copy of the table
- A CSV export
- A dashboard
Show answer & explanation
Correct: A — a view. A view stores a query definition, not data, so it presents a reusable logical result that always reflects the current underlying table — exactly the requirement.
Why not the others: copying the table (B) duplicates data and goes stale; a CSV export (C) is a static snapshot; a dashboard (D) displays results but is not a reusable query object. Views provide logical reuse without data duplication.
Source: Databricks — Views → Further reading: PowerKram — Querying with Databricks SQL →An analyst needs total sales per region from a sales table. Which SQL construct produces one summarized row per region?
- SELECT * FROM sales;
- SELECT region, SUM(amount) FROM sales GROUP BY region;
- SELECT region, amount FROM sales ORDER BY region;
- SELECT DISTINCT region FROM sales;
Show answer & explanation
Correct: B. Aggregating with SUM(amount) and GROUP BY region returns one row per region with its total — the standard aggregate-by-group pattern.
Why not the others: SELECT * (A) returns every raw row with no aggregation; ordering by region (C) sorts but does not summarize; SELECT DISTINCT region (D) lists regions but computes no totals. Only the GROUP BY aggregate answers the question.
An analyst wants to see how long a query took, how much data it scanned, and which stage was slowest, to optimize it. Where should they look?
- The dashboard sharing settings
- Query history and the query profile in Databricks SQL
- The Unity Catalog permissions tab
- The notebook revision history
Show answer & explanation
Correct: B. Query history records executed queries, and the query profile breaks down execution time, data scanned, and per-stage cost — the tools for analyzing and optimizing query performance.
Why not the others: dashboard sharing (A) controls access, not performance; Unity Catalog permissions (C) govern security; notebook revision history (D) tracks code changes, not SQL warehouse execution. Query history and profile are the analysis tools.
Source: Databricks — query history & profile → Further reading: PowerKram — Analyzing & Optimizing Queries →An analyst adds an area chart to a dashboard and sets its query parameter to a "Dashboard Parameter." What is the effect?
- The chart uses a fixed value set when added and cannot change afterward
- The chart, and every other visualization on the dashboard that uses the same parameter, responds to the shared dashboard-level parameter
- The chart converts into a parameter control
- Only that chart changes; other visualizations ignore the parameter
Show answer & explanation
Correct: B. A Dashboard Parameter is shared at the dashboard level, so the area chart and all other visualizations bound to that same parameter update together when the value changes.
Why not the others: a fixed unchangeable value (A) describes a static widget parameter, not a dashboard parameter; the chart does not convert into a control (C); and the parameter is shared, so other visualizations do not ignore it (D). Shared scope is the defining behavior.
Source: Databricks — dashboard parameters → Further reading: PowerKram — Dashboards & Visualizations →A business team wants to ask questions of their data in natural language and get SQL-backed answers and visualizations, curated by an analyst. Which Databricks capability provides this?
- A static PDF report
- An AI/BI Genie space
- A single SQL query saved to a folder
- A cluster init script
Show answer & explanation
Correct: B — an AI/BI Genie space. Genie spaces let business users ask natural-language questions that Databricks translates into SQL over curated datasets, with analysts configuring and maintaining the space — exactly the described need.
Why not the others: a static PDF (A) cannot answer new questions; a single saved query (C) is not an interactive natural-language interface; a cluster init script (D) is infrastructure configuration, unrelated to BI. Genie is the natural-language analytics feature.
Source: Databricks — AI/BI Genie → Further reading: PowerKram — Building AI/BI Genie Spaces →An analyst is modeling sales data for BI and wants a central fact table of transactions linked to descriptive dimension tables such as date, product, and store. Which modeling pattern is this?
- A single wide denormalized table only
- A star schema with a fact table joined to dimension tables
- Storing each row as a separate JSON file
- No model; query raw logs directly every time
Show answer & explanation
Correct: B — a star schema. A central fact table (transactions) surrounded by dimension tables (date, product, store) is the classic star schema, the standard analytics modeling pattern for BI queries.
Why not the others: a single wide table (A) can work but is not what "fact linked to dimensions" describes; per-row JSON files (C) defeat efficient analytic querying; querying raw logs with no model (D) is not data modeling at all. The fact-plus-dimensions structure is a star schema.
Source: Databricks — data modeling →An analyst needs to grant a colleague read-only access to a specific table governed by Unity Catalog. Which approach follows best practice?
- Email the colleague an export of the table
- GRANT SELECT on the table to the colleague (or their group) in Unity Catalog
- Give the colleague the workspace admin account password
- Make the table public to everyone
Show answer & explanation
Correct: B. Granting SELECT on the specific table to the user or, better, their group through Unity Catalog provides least-privilege, auditable read-only access — the governance best practice.
Why not the others: emailing an export (A) bypasses governance and creates an uncontrolled copy; sharing an admin password (C) is a serious security violation; making the table public (D) over-exposes the data. Scoped GRANTs in Unity Catalog are the correct mechanism.
Source: Databricks — Unity Catalog privileges →Keep going: Learning & Career resources
This certification pays off fastest when it sits on top of real platform skills and a clear sense of where the role leads. Two PowerKram hubs back this exam up.
Deep dive: exam structure, scoring, study path & recertification
Exam structure and how it’s scored
The exam delivers 45 scored multiple-choice questions in 90 minutes; additional unscored items may appear for calibration, with extra time factored in. Databricks does not publish a fixed numeric passing score on the official exam page, and your result is reported as pass or fail. Questions are scenario-based and SQL-heavy — many present a query or task and ask you to choose the correct statement or fix an error — and all SQL adheres to ANSI standards. Read the exam-format deep dive →
What the nine domains actually test, and what changed
Executing queries (20%), Creating dashboards and visualizations (16%), and Analyzing queries (15%) together make up over half the exam, so SQL fluency and the Databricks SQL UI are where most preparation belongs. AI/BI Genie spaces (12%) is a newer, substantial domain, and platform understanding (11%) plus Unity Catalog work across Managing Data (8%) and Securing Data (8%) round it out, with Importing Data and Data Modeling at 5% each. The exam was revised to nine domains, so older five-domain study notes can mislead. Read the Databricks SQL toolchain guide →
Realistic study path
Plan roughly four to eight weeks depending on SQL background. A workable path: the Databricks Academy data-analyst learning path, then consistent hands-on practice in Databricks SQL — write queries and views, build a dashboard with parameters, explore Unity Catalog (catalogs, schemas, permissions, lineage), and create an AI/BI Genie space. Practice in the actual UI, since some questions are navigation-specific, and make sure your ANSI SQL (joins, aggregates, window functions) is solid. Read the study plan →
Cost, scheduling, and delivery
The registration fee is $200 USD plus applicable local taxes. The exam is proctored and can be taken online or at a test center, and is offered in English. Online delivery requires a quiet private space and a system check through the proctoring provider. Databricks periodically offers discount vouchers through learning events. Verify current fees and scheduling on Databricks’ official page before booking. Databricks’ official certification page →
Recertification
The certification is valid for two years. To stay certified you retake and pass the current version of the exam before it expires — there is no continuing-education-credit alternative. Because Databricks refreshes the exam to track platform changes (the recent revision added AI/BI Genie and reorganized the domains), recertifying also keeps your validated skills current. Read the recertification guide →
Career outlook
Data analysts with lakehouse-platform expertise are in growing demand as organizations modernize their analytics stacks, and a platform-specific associate credential signals practical SQL and BI competence on Databricks. The credential is most valuable paired with demonstrable work — published dashboards, well-modeled datasets, a maintained Genie space. For salary ranges and role-specific paths, see the Career Hub. Career Hub — Data Analyst →
Frequently asked questions
Is the Databricks Data Analyst Associate exam hard?
It is an associate-level exam and most candidates with solid SQL find it fair, but it is applied rather than theoretical. The questions are scenario-based and often present a query or a Databricks SQL task and ask you to pick the correct statement or fix an error, so comfort writing ANSI SQL and navigating the Databricks SQL UI matters more than memorizing definitions. The query, dashboard, and analysis domains together are over half the exam.
What is the passing score?
Databricks does not publish a fixed numeric passing score on the official exam page; results are reported as pass or fail based on overall performance across all questions. You may see "70%" quoted on third-party sites, but that figure is not confirmed by Databricks, so treat it as unofficial and aim to be comfortable across every domain rather than targeting a specific percentage.
How many domains are on the exam? I’ve seen different numbers.
The current exam has nine domains: Understanding the Databricks Platform (11%), Managing Data (8%), Importing Data (5%), Executing Queries (20%), Analyzing Queries (15%), Creating Dashboards and Visualizations (16%), Developing AI/BI Genie spaces (12%), Data Modeling (5%), and Securing Data (8%). An earlier version of the exam used a smaller set of domains, which is why you may find conflicting lists online. Study against the current nine-domain structure in the official exam guide.
Do I need experience or prerequisites to take it?
There are no formal prerequisites, so anyone can register. Databricks recommends about six months of hands-on experience with Databricks SQL tools. Strong ANSI SQL fundamentals, familiarity with Unity Catalog, and comfort building dashboards close most of the gap. If you are new to Databricks, plan a few weeks of daily hands-on practice in a workspace.
Which topics should I focus on most?
Prioritize the query-heavy domains: Executing Queries (20%), Creating Dashboards and Visualizations (16%), and Analyzing Queries (15%) are over half the exam. Make sure your SQL — joins, aggregations, views, window functions — is solid, and practice building dashboards with parameters and analyzing queries with query history and the query profile. Then cover Unity Catalog for Managing and Securing Data, and spend time in an AI/BI Genie space.
How does it differ from the Data Engineer Associate and ML Associate exams?
The Data Analyst Associate is about analyzing data with Databricks SQL — querying, dashboards, BI, and governance from an analyst's seat. The Data Engineer Associate focuses on building ETL pipelines, Delta Lake, and orchestration. The ML Associate covers machine learning tasks with AutoML, MLflow, and Spark ML. Choose the analyst exam if your work is SQL- and BI-centric rather than pipeline- or model-centric.
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