Exam Code: Databricks Certified Data Analyst Associate
Exam Name: Databricks Certified Data Analyst Associate Exam
Price: $68.00 $58.88
Exam Questions: 118 Q&As
Last Updated: 2026-10-07
Prepare for the Databricks Certified Data Analyst Associate exam with practice covering Unity Catalog, data ingestion, Databricks SQL, query analysis, AI/BI dashboards, Genie spaces, data modeling, and security.
The certification validates your ability to complete foundational analytics work on the Databricks Data Intelligence Platform, from finding governed data to delivering queries, visualizations, dashboards, and natural-language analytics experiences.
The exam assesses whether you can use the Databricks Data Intelligence Platform for basic data analysis. This includes discovering governed datasets, importing and preparing data, executing SQL, investigating query behavior, building dashboards, maintaining Genie spaces, modeling analytical data, and applying security controls.
No formal prerequisite is required. Databricks recommends related training and at least six months of hands-on experience performing the tasks in the exam guide. The credential remains valid for two years, after which recertification requires passing the current live exam.
Strong candidates know which interface or SQL feature solves the stated problem with the least unnecessary work. They can separate data discovery from permission management, query execution from query analysis, and a fixed dashboard from a conversational Genie experience.
Forty-five scored questions allow about two minutes per item. Answer direct platform and SQL questions first, mark longer scenarios for review, and reserve the final minutes for questions involving permissions, joins, aggregation grain, query plans, or similar-looking product features.
Understand the platform's core analytics capabilities, workspace experiences, lakehouse concepts, Delta Lake, compute options, and the relationship between data, AI, governance, and SQL workloads.
Use Unity Catalog and Catalog Explorer to discover, query, clean, certify, and manage governed datasets. Know how metadata, ownership, lineage, privileges, and certified assets help analysts find trustworthy data.
Select an appropriate ingestion method from UI uploads, cloud object storage such as Amazon S3, Delta Sharing, APIs, Auto Loader, and Databricks Marketplace. Focus on source, volume, frequency, and whether the data must be copied or shared in place.
This is the largest domain. Create views, filter and sort results, aggregate data, combine tables with joins, use SQL functions, and select or operate SQL warehouse resources for analytical workloads.
Use query history, Query Profile, audit information, logs, and table history to understand performance and activity. Recognize when layout and clustering, including Liquid Clustering, can improve data access patterns.
Choose suitable visualizations, configure datasets and filters, arrange dashboard content, publish and share AI/BI dashboards, and understand how viewer permissions affect access to the dashboard and its data.
Prepare trusted data and instructions for natural-language analysis, test common questions, refine the space, and share it with the intended users. A useful Genie space needs clear business context, not just table access.
Model data at the correct grain, define relationships, and use tables, views, joins, and reusable business logic to support consistent analysis without duplicating or miscounting records.
Apply least privilege through Unity Catalog ownership and grants, and consider legal and organizational rules for sensitive data. Understand how catalogs, schemas, tables, views, users, groups, and service principals fit the governance model.
Catalog Explorer versus workspace access: Unity Catalog governs data objects; workspace permissions govern workspace assets. Query execution versus analysis: a SQL warehouse runs the query, while Query Profile and history explain its behavior. Dashboard versus Genie: dashboards present curated visuals; Genie supports governed natural-language exploration.
Days 1 to 3: platform, Delta Lake, Unity Catalog, Catalog Explorer, and ingestion. Days 4 to 7: SQL warehouses, filtering, joins, aggregation, views, and functions. Days 8 and 9: Query Profile, history, logs, and Liquid Clustering. Days 10 and 11: dashboards and visualizations. Day 12: Genie. Day 13: modeling and security. Day 14: complete a timed 45-question set.
These questions use current terminology where possible. Catalog Explorer is the current name for the experience called Data Explorer in older material. The examples are independent practice, not official Databricks exam questions.
Which considerations should a data analyst evaluate when working with personally identifiable information (PII)?
Correct answer: E
Explanation: PII handling must account for the organization's policies and the legal obligations that apply to collection, storage, access, processing, and analysis. A technical permission alone does not establish that a use is compliant.
Analyst checkpoint: Confirm data classification, approved purpose, applicable locations, access scope, retention, and whether masking or de-identification is required before beginning analysis.
Common mistake: Checking only the law where the analyst works. Data can remain subject to requirements tied to the people, collection location, storage location, or organization.
Why the other options are wrong: A, B, and C are individually relevant but incomplete. D ignores both legal and organizational controls.
Delta Lake stores table data as a series of data files. Which additional information is stored alongside those files?
Correct answer: C
Explanation: Delta Lake extends Parquet data files with a file-based transaction log. The log records table metadata, versions, and the data-file actions needed to determine the valid state of the table.
Analyst checkpoint: Separate storage-layer metadata from interface features. A visualization or displayed owner may be associated with a platform object, but neither is stored as a Delta table visualization beside the data files.
Common mistake: Selecting B because Catalog Explorer can show metadata and ownership. What the UI displays is not the same as what the Delta transaction log stores.
Why the other options are wrong: A ignores the transaction log. D is a presentation artifact. E is governed through the catalog rather than stored as the only item beside the table's data files.
Which capability is a key advantage of a Delta Lake-based lakehouse compared with a basic data lake that stores files without a transaction layer?
Correct answer: A
Explanation: The Delta transaction log provides atomicity, consistency, isolation, and durability for table operations. A transaction commits as a valid table version or fails without making a partial state visible.
Analyst checkpoint: Ask which option distinguishes the transaction layer. Object storage can scale and use open formats without Delta Lake, but those properties alone do not provide transactional table guarantees.
Common mistake: Choosing D because lakehouses are designed for large datasets. Scalability is valuable, but it is not unique to Delta Lake.
Why the other options are wrong: B, C, D, and E can exist in ordinary data lake solutions. ACID behavior is the distinguishing benefit in this list.
Which Databricks SQL benefit is provided by Catalog Explorer (formerly Data Explorer)?
Correct answer: B
Explanation: Catalog Explorer helps users find and inspect data assets. Depending on privileges, they can review schemas, table details, sample data, lineage, ownership, and permissions, and manage Unity Catalog objects.
Analyst checkpoint: Catalog Explorer capabilities remain permission-aware. Seeing an object through BROWSE does not automatically grant SELECT, ownership, or permission-management rights.
Common mistake: Selecting C because data exploration sounds like dashboard work. Catalog Explorer discovers and governs data assets; AI/BI dashboards present analytical results.
Why the other options are wrong: A ignores table privileges. C and D describe dashboard or visualization experiences. E is broader than Catalog Explorer's data-governance purpose.
A data analyst owns the Unity Catalog managed table my_table. The analyst wants to transfer ownership to one other user by using Catalog Explorer. Which approach should the analyst use?
Correct answer: C
Explanation: An authorized current owner can use Catalog Explorer to transfer a table to the selected principal. Ownership moves to the new user rather than becoming empty or being granted to everyone.
Analyst checkpoint: Ownership is not the same as SELECT or MODIFY. The owner can manage the securable object and its privileges, so transfer it only to the intended user, service principal, or group permitted by the governance design.
Common mistake: Choosing D because administrators can manage many objects. The requirement names a single other user, so assigning an unrelated group does not satisfy it.
Why the other options are wrong: A leaves no valid replacement. B grants ownership too broadly. D selects the wrong principal. E revokes access rather than transferring ownership.
Content is reviewed when Databricks updates the official exam guide or documentation for Unity Catalog, Databricks SQL, AI/BI, Genie, and related platform features. Check the product page for its latest update date.
Yes. The five examples above include governance, Delta Lake, transaction guarantees, Catalog Explorer, and ownership scenarios with explanations and distractor analysis.
No. They are independently prepared study material. CertQuestionsBank is not affiliated with or endorsed by Databricks.
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Content is reviewed against the current exam guide and product documentation. Older terms may be identified when they help learners recognize a renamed interface, such as Data Explorer and Catalog Explorer.
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Review governed data discovery, ingestion, SQL, query analysis, dashboards, Genie spaces, modeling, and security.
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