Databricks-DA-Assoc Understanding the Databricks Platform Practice Test 3 — 15 Questions

Question 1

An analyst opens the Databricks SQL editor and wants to browse the tables available in the workspace's Unity Catalog metastore before writing a query. Which workspace object should the analyst use to navigate catalogs, schemas, and tables?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The Data Explorer in the Databricks workspace. The Data Explorer is the built-in workspace UI for browsing Unity Catalog objects, letting analysts navigate catalogs and schemas, inspect table metadata, and preview data. Before writing SQL, an analyst can confirm table names and columns there. The other listed surfaces handle Git integration, compute configuration, or job monitoring, none of which expose the metastore hierarchy needed for table discovery.

Answer

The Cluster configuration page

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The Repos folder in the workspace sidebar

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The Jobs run history UI

Question 2

A data analyst needs to run a scheduled SQL query every morning and deliver the result to a finance team as a CSV file in cloud storage. The query logic is already tested in a Databricks SQL query. Which Databricks capability should the analyst use to automate this delivery?

Accepted Answer

A Databricks SQL query schedule with a destination. Databricks SQL supports scheduling saved queries so they execute on a recurring cadence, and a schedule can include a destination such as cloud storage or email. That combination automates both execution and delivery of CSV results, exactly matching the finance team's requirement. Alerts, widgets, and Delta Live Tables address monitoring, interactivity, and pipeline ETL rather than scheduled result delivery.

Answer

A Databricks SQL alert

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A Delta Live Tables pipeline

Answer

A notebook-scoped widget parameter

Question 3

A data analyst at a retail company has been asked to build interactive sales reports that refresh automatically each morning and can be shared with regional managers. The analyst wants to use Databricks SQL to create queries, schedule them, and publish visualizations without writing notebook code. Which Databricks component should the analyst use to accomplish this?

Accepted Answer

Databricks SQL. Databricks SQL is purpose-built for SQL analysts: it includes a query editor, visualization tools, dashboards, alerts, and scheduled refreshes running on SQL warehouses. Because the analyst needs to author queries, schedule them, and publish shareable visualizations without notebook code, this is the only listed component that covers the full workflow end to end.

Answer

Databricks Jobs

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Databricks Marketplace

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Databricks Repos

Question 4

A data analyst at a retail company needs a serverless, fully managed SQL environment in Databricks to run BI queries against a gold Delta table. The team has no interest in managing clusters, and the queries must start instantly without a warm-up period. Which Databricks SQL warehouse type should the analyst select?

Accepted Answer

Serverless SQL warehouse. The requirement is a fully managed SQL endpoint that starts immediately and requires no cluster sizing, which is exactly what a serverless SQL warehouse provides. It runs on Databricks-managed compute, scales automatically for BI workloads, and eliminates warm-up and capacity planning. Classic and Pro warehouses and all-purpose clusters all involve customer-managed compute.

Answer

All-purpose cluster with the SQL dialect enabled

Answer

Pro SQL warehouse with Photon enabled

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Classic SQL warehouse with a fixed cluster size

Question 5

A data analyst needs to query a table that contains sensitive customer financial data. Company policy requires that analysts see only masked values for account numbers in query results, and the masking must apply regardless of which tool or user queries the table. Which Databricks capability should be used to enforce this consistently at the data layer?

Accepted Answer

Column masks applied to the table. Column masks attach a masking expression directly to a table column, so the transformation is evaluated at query time for every read, independent of the client or user. This data-layer enforcement is exactly what the policy demands: unauthorized analysts receive masked account numbers whether they query via a notebook, the SQL editor, or a BI tool, and authorized users can still see real values.

Answer

A view that filters rows before returning results

Answer

Workspace-level notebook permissions

Answer

Cluster access mode configuration

Question 6

A data analyst is new to a Databricks workspace and needs to understand which compute options are available for running SQL and notebooks. Which TWO of the following statements accurately describe Databricks compute in this context? (Choose two.)

Accepted Answer

SQL warehouses are optimized for running SQL and serving dashboards and scheduled queries.. SQL warehouses are purpose-built for SQL, dashboards, and scheduled queries, while job clusters are ephemeral compute created for a specific run and terminated afterward. Together these describe two real compute behaviors an analyst should know. The other statements misstate requirements or capabilities, such as claiming all-purpose clusters are needed for the SQL editor or that serverless cannot use Unity Catalog.

Answer

All-purpose clusters are required to run any query in the Databricks SQL editor.

Answer

SQL warehouses and all-purpose clusters share the same configuration settings and scaling behavior.

Answer

Serverless compute for notebooks cannot be used with Unity Catalog.

Question 7

An analyst runs a notebook against an all-purpose cluster and notices that the first cell, which reads a large Delta table, takes several minutes while subsequent similar queries finish quickly. The analyst wants to understand why this happens and ensure the same quick response on later runs. Which explanation best describes the underlying behavior?

Accepted Answer

The cluster had to start and initialize, and Delta caching kept data in memory for later queries. The slow first cell reflects two overlapping costs: the all-purpose cluster must start and initialize before any query runs, and the initial scan reads Delta files from remote storage. Once that scan completes, Databricks caches the data on local SSD, so later queries that touch the same files avoid remote reads. Warm compute plus caching explains the dramatic speedup on subsequent runs.

Answer

The first query compiled a query plan that Databricks permanently stored in the table metadata

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The first query used a different execution engine, and later queries switched to Photon

Answer

The Delta table was automatically optimized after the first read, rewriting all files

Question 8

A data analyst has a notebook that reads a Delta table and produces summary statistics. The analyst wants colleagues to see the latest results in a dashboard without rerunning the notebook manually each time. Which Databricks capability should the analyst use to keep the dashboard current?

Accepted Answer

Convert the notebook into a Databricks SQL dashboard and schedule the underlying query. Databricks SQL dashboards are built on saved queries that can be scheduled to refresh, so viewers see current results without anyone rerunning a notebook. Recreating the summary logic as a query and scheduling it automates currency. Documentation, larger clusters, and static exports do not refresh a dashboard, so they fail to meet the requirement of up-to-date shared results.

Answer

Add a markdown cell describing how to run the notebook

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Increase the cluster size used by the notebook

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Export the notebook results to a static PDF for distribution

Question 9

A data analyst is new to a Databricks workspace and needs to work with data stored in Unity Catalog. The analyst wants to understand which statements about Unity Catalog are accurate. (Choose two.)

Accepted Answer

Unity Catalog access control can be applied at the catalog, schema, table, and column levels, including row and column filters.. Unity Catalog centralizes governance with a three-level namespace and supports privileges from the catalog level down to columns, including row filters and column masks. These two characteristics let an analyst reference data with fully qualified names and trust that access rules are enforced consistently. The other statements misstate storage requirements, metadata placement, or the range of supported query interfaces.

Answer

Unity Catalog metadata is stored separately in each workspace, so grants must be repeated for every workspace.

Answer

Unity Catalog can only be queried from notebooks and not from Databricks SQL warehouses or dashboards.

Answer

Unity Catalog requires that all tables be stored as external tables in cloud object storage rather than managed tables.

Question 10

An analyst wants to include a chart from a Databricks SQL query in a presentation and also allow stakeholders to explore the same query interactively later. Which two-part approach best fits this need?

Accepted Answer

Download the chart as an image for the presentation, and share the saved query or dashboard for interactive exploration. The scenario needs both a static visual for slides and a live, explorable view for stakeholders. Exporting the chart as an image handles the presentation, and sharing the saved query or dashboard provides governed interactivity. Copying code, distributing notebooks, or screenshotting the editor fails to produce a proper chart and does not support shared exploration.

Answer

Create a separate notebook for each stakeholder and email the notebook files

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Take a screenshot of the SQL editor and paste it into the slides

Answer

Copy the query text into the presentation slides and ask stakeholders to run it themselves

Question 11

A data analyst joined a new Databricks workspace and sees a catalog named 'sales_prod' containing schemas 'bronze', 'silver', and 'gold'. The analyst needs to query only the 'gold' schema tables and must not see or query the 'bronze' or 'silver' tables. Which Unity Catalog object should the workspace administrator grant to satisfy this least-privilege requirement?

Accepted Answer

USE CATALOG on sales_prod and USE SCHEMA plus SELECT on the gold schema. Unity Catalog enforces a privilege hierarchy where traversing a catalog requires USE CATALOG and reading a schema's tables requires USE SCHEMA and SELECT on that schema. Granting USE CATALOG on sales_prod and USE SCHEMA plus SELECT on gold gives read access to only the gold tables, while the absence of grants on bronze and silver keeps them hidden.

Answer

USE SCHEMA on bronze and silver only

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SELECT on the sales_prod catalog

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ALL PRIVILEGES on the sales_prod catalog

Question 12

A data analyst is building a dashboard in Databricks SQL and needs to give viewers the ability to change the date range and a region filter without editing the underlying query. The dashboard should refresh results based on the viewer's selections. Which feature should the analyst use?

Accepted Answer

Dashboard parameters that are referenced in the dataset queries. Dashboard parameters are the built-in way to let viewers supply values such as a date range or region that are injected into dataset queries. They provide interactive controls without editing SQL and refresh results on selection. Materialized views, Delta Live Tables, and notebook widgets address performance, pipeline refresh, or notebook interactivity, not dashboard viewer filtering.

Answer

A materialized view that pre-aggregates the data by date and region

Answer

A Delta Live Tables pipeline that refreshes the dashboard dataset hourly

Answer

A notebook widget embedded in the dashboard as a separate visualization

Question 13

A data analyst needs to grant a colleague the ability to run an existing Databricks SQL query and view its results, but the colleague must not be able to edit the query text or change its schedule. The query is saved in the workspace. Which permission level should the analyst assign on the query object?

Accepted Answer

CAN RUN. CAN RUN is the least-privilege permission that lets a user execute a saved query and view results while preventing edits to the query text or schedule. CAN VIEW does not grant run capability, while CAN EDIT and CAN MANAGE both allow modifications that the scenario explicitly forbids.

Answer

CAN EDIT

Answer

CAN VIEW

Answer

CAN MANAGE

Question 14

A data analyst is querying a Unity Catalog managed table named sales in the analytics catalog and the marketing schema. A query fails with an error indicating the table cannot be found, even though the analyst can see the table in Catalog Explorer. The analyst is connected to a SQL Warehouse in the same workspace. Which action is most likely to resolve the access issue?

Accepted Answer

Verify that the analyst has USE CATALOG on analytics, USE SCHEMA on marketing, and SELECT on the sales table. Unity Catalog enforces a privilege hierarchy: USE CATALOG on the catalog, USE SCHEMA on the schema, and SELECT on the table. Visibility in Catalog Explorer does not imply these grants. Restarting the warehouse, converting the table to external, or enabling legacy cluster table access control do not address missing Unity Catalog privileges.

Answer

Restart the SQL Warehouse so it picks up the latest Unity Catalog metadata

Answer

Ask a workspace administrator to enable table access control on the all-purpose cluster

Answer

Convert the managed table to an external table so the analyst can read the underlying files

Question 15

A data analyst has a Databricks SQL dashboard that reads a table in the 'finance' catalog. The dashboard works for the analyst but shows an error for a colleague who has SELECT on the table. The colleague lacks USE CATALOG on 'finance' and USE SCHEMA on the containing schema. What is the most likely cause of the error the colleague sees?

Accepted Answer

The colleague cannot traverse the catalog and schema, so Unity Catalog denies resolution of the table even though SELECT is granted. Unity Catalog resolves objects through their full three-part name, which requires USE CATALOG on the catalog and USE SCHEMA on the schema before SELECT on the table is even evaluated. The colleague holds SELECT but lacks the traversal privileges, so name resolution fails and the dashboard query errors until those grants are added.

Answer

The dashboard was created with the analyst's credentials embedded, so it always runs as the analyst

Answer

The colleague is connected to a different SQL warehouse that cannot reach the finance catalog

Answer

The table is a view that references tables the colleague cannot access