Databricks Data Analyst Exam Flashcards

Q: How many questions are on the Databricks Data Analyst Associate exam?

The official exam page lists 45 scored multiple-choice questions with a 90-minute time limit. Databricks notes that unscored items may also appear, and extra time is factored in to cover them.

Q: What is the passing score for Databricks Data Analyst Associate?

The Databricks Academy FAQ sets the passing score at an unrounded 70.00% across certification exams. On a 45-question form, that works out to roughly 32 correct answers.

Q: Why do these flashcards weight some domains more heavily than others?

The official blueprint weights Executing Queries at 20%, Dashboards and Visualizations at 16%, Analyzing Queries at 15%, and AI/BI Genie Spaces at 12% - together 63% of the exam. This 50-card set allocates cards proportionally so your review time matches the real exam weighting.

Q: What happens if I fail the exam and need to retake it?

Under Databricks' certification retake policy (effective November 1, 2023), you must wait 14 days between any two exam attempts, with no limit on total attempts and no different rule after multiple failures. Each attempt is charged at the full exam price.

Q: Do I need hands-on Databricks experience to use these cards effectively?

Yes. Databricks recommends related training plus at least six months of hands-on experience performing analyst tasks. These cards work best as reinforcement for concepts you have practiced in Unity Catalog, Databricks SQL, dashboards, and Genie, not as a substitute for hands-on time.

Question 1

What is the top-to-bottom Unity Catalog object hierarchy for a table?

Accepted Answer

Metastore -> Catalog -> Schema -> Table (or View/Volume/Function/Model). Every governed object is addressed with a three-level catalog.schema.object namespace.

Question 2

What does Catalog Explorer let an analyst do?

Accepted Answer

Browse catalogs, schemas, tables, and volumes, and inspect permissions, tags, comments, and lineage without leaving the workspace UI.

Question 3

What is Databricks Marketplace used for?

Accepted Answer

An open marketplace for discovering and subscribing to shared data, notebooks, and AI assets from providers outside your own workspace.

Question 4

What does a lineage graph show, and why does an analyst check it?

Accepted Answer

It traces upstream sources and downstream consumers of a table or dashboard, letting an analyst confirm where data comes from and what would break if it changed.

Question 5

What are Unity Catalog system tables?

Accepted Answer

Built-in, queryable tables that expose operational metadata such as audit logs, billing, and lineage, so admins and analysts can monitor platform usage with SQL.

Question 6

What does 'Data Intelligence Platform' mean in the Databricks product story?

Accepted Answer

A single lakehouse platform that unifies data engineering, SQL analytics, BI, and AI/ML, governed end-to-end by Unity Catalog.

Question 7

What is a Certified tag on a Unity Catalog asset?

Accepted Answer

A steward-reviewed label marking a dataset as trusted and production-ready; it signals review, not an automatic guarantee of data quality.

Question 8

How does a custom tag differ from a certified tag?

Accepted Answer

A custom tag is any searchable key-value metadata label an owner adds for discovery; it carries no built-in stewardship guarantee like a certified tag does.

Question 9

COALESCE vs NULLIF: what does each do?

Accepted Answer

COALESCE(a, b) returns the first non-null value; NULLIF(a, b) returns NULL if a equals b, otherwise returns a. Both are used to clean up data in SQL.

Question 10

How would you clean up inconsistent text values in a governed column with SQL?

Accepted Answer

Use REGEXP_REPLACE to pattern-match and rewrite inconsistent substrings, often paired with a lineage check to confirm which upstream source introduced the issue.

Question 11

Auto Loader vs COPY INTO: when do you pick each for file ingestion?

Accepted Answer

Auto Loader incrementally and automatically ingests new cloud files as they arrive; COPY INTO is a simpler, idempotent SQL command better suited to scheduled or one-time batch loads.

Question 12

Delta Sharing vs Marketplace: what's the difference in how you get external data?

Accepted Answer

Delta Sharing is a direct, point-to-point secure share from a specific provider that works cross-platform; Marketplace is a public or private catalog of listings you browse and subscribe to.

Question 13

Rank the three Databricks SQL warehouse types by startup speed.

Accepted Answer

Serverless starts in seconds, Pro takes roughly four minutes to provision, and Classic is the slowest, entry-level tier.

Question 14

Which SQL warehouse type supports Intelligent Workload Management (IWM) autoscaling?

Accepted Answer

Only Serverless warehouses use AI-based IWM to scale compute to bursty, unpredictable analyst workloads; Pro and Classic do not.

Question 15

Managed table vs external table: what happens on DROP TABLE?

Accepted Answer

Dropping a managed table deletes the underlying data files because Unity Catalog owns them. Dropping an external table removes only the metadata reference; the files in your own storage location remain.

Question 16

View vs materialized view: what's the key tradeoff?

Accepted Answer

A view recomputes its query logic on every read, so it is always fresh but can be slow. A materialized view precomputes and stores the result, refreshing on a batch schedule, so reads are fast but can be stale.

Question 17

When would you reach for a streaming table instead of a materialized view?

Accepted Answer

Use a streaming table for continuous, append-only ingestion where each row is processed exactly once, typically at the Bronze layer; use a materialized view for batch-refreshed, BI-ready aggregations at Silver or Gold.

Question 18

What does PIVOT do in Databricks SQL?

Accepted Answer

It rotates unique values from one column into multiple output columns, turning long, row-based data into a wide cross-tab summary.

Question 19

What do the higher-order functions FILTER and TRANSFORM operate on?

Accepted Answer

Both operate element-by-element on array columns: FILTER keeps only elements matching a lambda condition, and TRANSFORM applies a lambda to map each element to a new value.

Question 20

How do you query a Delta table as it existed at a specific past version?

Accepted Answer

Use time travel syntax such as SELECT * FROM table VERSION AS OF n (or TIMESTAMP AS OF), which reads the table's historical state without restoring or copying data.

Question 21

UNION vs UNION ALL: which keeps duplicate rows?

Accepted Answer

UNION ALL keeps every row from both queries, including duplicates. Plain UNION removes duplicate rows, which costs extra computation to de-duplicate.

Question 22

What lets Databricks SQL query data that lives outside the lakehouse in another system?

Accepted Answer

Federated querying (Lakehouse Federation) lets Databricks SQL query external databases directly through Unity Catalog without first copying the data in.

Question 23

Query History vs Query Profile: what does each show?

Accepted Answer

Query History lists past query runs with duration and status, a list view. Query Profile drills into one execution's stage-level timings and bottlenecks, a deep-dive view.

Question 24

Result cache vs disk cache: what does each speed up?

Accepted Answer

The result cache reuses the output of an identical prior query with no recomputation. The disk cache stores remote file reads locally on the warehouse, speeding up repeated scans of the same files even if the query text differs.

Question 25

What problem does Liquid Clustering solve for query performance?

Accepted Answer

It provides flexible, incrementally-maintained data layout on a table instead of fixed partitioning, so queries filtering on frequently changing columns skip more files and run faster.

Question 26

How do you audit the write history of a Delta table?

Accepted Answer

Run DESCRIBE HISTORY on the table to see every version, operation, timestamp, and user that modified it.

Question 27

What does 'spill to disk' in a Query Profile indicate?

Accepted Answer

It means an operation such as a join or sort exceeded available memory and had to write intermediate data to disk, a strong sign of a slow, under-resourced query.

Question 28

What is Photon, and where does it apply?

Accepted Answer

Photon is Databricks' vectorized, native query execution engine that accelerates SQL and DataFrame workloads across all SQL warehouse tiers; it is an engine, not a separate warehouse type.

Question 29

What does Predictive I/O optimize during query execution?

Accepted Answer

It uses learned statistics to intelligently skip irrelevant files and rows before scanning, reducing the I/O a query needs to perform.

Question 30

A query is unexpectedly slow and you suspect a bad join. Where do you look first, and then next?

Accepted Answer

Start in Query History to confirm the run and its duration, then open its Query Profile to see stage-level timings and identify which join or shuffle stage is the bottleneck.

Question 31

What is a dashboard parameter used for?

Accepted Answer

A reusable, user-facing input, such as a date range or region picker, that filters every widget on a dashboard at once without editing each underlying query.

Question 32

How does scheduled refresh differ from opening a dashboard on demand?

Accepted Answer

Scheduled refresh automatically re-runs the underlying queries at set intervals so viewers always see recently computed data, instead of requiring a manual reload.

Question 33

What triggers a dashboard alert?

Accepted Answer

An alert fires a notification automatically when a query result crosses a defined threshold condition, without a person needing to be watching the dashboard.

Question 34

What is a shareable link on an AI/BI Dashboard?

Accepted Answer

A controlled-access link that lets you distribute a dashboard to a defined external or internal audience without granting them full workspace access.

Question 35

What does embedding a dashboard accomplish?

Accepted Answer

It surfaces the live AI/BI Dashboard inside another application or portal, so stakeholders view it without opening the Databricks workspace directly.

Question 36

Why use a multi-page dashboard layout?

Accepted Answer

It organizes related visuals into separate pages within one dashboard, keeping a large set of metrics navigable instead of cramming everything onto one screen.

Question 37

Notebook chart vs AI/BI Dashboard: which is for recurring stakeholder reporting?

Accepted Answer

A notebook chart is a one-off visualization living inside a single cell for exploration. An AI/BI Dashboard is the shareable, scheduled, alertable artifact meant for recurring stakeholder use.

Question 38

What governs who can view or edit a published dashboard?

Accepted Answer

Dashboard permissions, set independently from the underlying tables' Unity Catalog grants, control which users or groups can view, edit, or manage the dashboard itself.

Question 39

What role do 'instructions' play in a Genie space?

Accepted Answer

They teach Genie the business-specific terms, definitions, and context it needs to correctly interpret natural-language questions against your data.

Question 40

Why add sample questions to a Genie space?

Accepted Answer

They show users example prompts they can ask, guiding them toward the kinds of questions the space is curated to answer well.

Question 41

What are 'trusted assets' in a Genie space?

Accepted Answer

Verified queries or views that Genie is instructed to prioritize when answering, improving accuracy over letting it generate SQL from scratch every time.

Question 42

How do benchmarks help improve a Genie space over time?

Accepted Answer

Benchmarks track answer quality against a known set of questions and expected results, letting an owner measure and improve accuracy as the space evolves.

Question 43

What should you do when users flag a Genie answer as wrong?

Accepted Answer

Use the built-in feedback mechanism to capture the flag, then adjust instructions, trusted assets, or curated datasets to correct the underlying cause.

Question 44

Why does a Genie space use a curated, scoped set of datasets instead of the whole catalog?

Accepted Answer

Limiting Genie to curated datasets keeps its answers grounded in vetted, relevant tables and reduces the chance it queries the wrong or ungoverned data.

Question 45

Star schema vs snowflake schema: what's the core tradeoff?

Accepted Answer

A star schema denormalizes dimension tables for fewer joins and faster BI reads. A snowflake schema normalizes dimensions further, reducing redundancy at the cost of more joins.

Question 46

How does the medallion architecture relate to data modeling choices?

Accepted Answer

Bronze holds raw ingested data, Silver holds cleaned and joined data, and Gold holds curated, BI-ready models such as star schemas; modeling patterns typically get applied at the Gold layer.

Question 47

What is the three-level namespace in Unity Catalog?

Accepted Answer

Every object is addressed as catalog.schema.table (or view/volume/function), giving a consistent governed path instead of a bare table name.

Question 48

RBAC vs row filters/column masks: what's the difference in what they restrict?

Accepted Answer

RBAC (GRANT/REVOKE) is all-or-nothing object-level access control. Row filters and column masks go further, restricting which specific rows are visible or obscuring specific column values for users who already have object access.

Question 49

Who controls whether other users can grant access to a Unity Catalog object?

Accepted Answer

The object's owner, or a user with the MANAGE privilege, controls granting; ownership is the root of delegated access control in Unity Catalog.

Question 50

How do you protect a column containing PII while still letting analysts query the table?

Accepted Answer

Apply a column mask so the raw PII value is obscured (or a row filter to hide rows entirely) for users without clearance, while the table itself remains queryable.

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