Databricks-DA-Assoc Practice Test 3 — 10 Questions

Question 1

A data analyst is using the Databricks Add Data UI to import a CSV file from cloud storage into a Delta table. The analyst wants to ensure the import process handles the data correctly. Which two actions can the analyst perform directly in the Add Data UI? (Choose two.)

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

Accepted Answer

Specify a custom delimiter for the CSV file.. The Add Data UI allows analysts to preview data, adjust column types, and specify a custom delimiter during import. It does not support scheduling, DDL schema definition, or schema evolution, which are features of other tools like Auto Loader or Jobs.

Answer

Set up a scheduled job to automatically ingest new files from the same location.

Answer

Enable schema evolution for future files.

Answer

Define a custom schema using DDL statements.

Question 2

A dashboard author wants viewers to click a bar in a Databricks SQL chart and have the dashboard display a second chart filtered to that bar's category. Which feature should the author configure to achieve this interactive behavior?

Accepted Answer

Dashboard cross-filtering or a dashboard-level filter driven by the selected value. Interactive cross-chart behavior comes from dashboard filtering driven by a selected value, so clicking a bar's category updates the other chart. Scheduled refresh only reloads data, a parameter default is static, and separate per-category queries create static charts, none of which respond to a viewer's selection.

Answer

A scheduled refresh on the dashboard so the second chart reloads on an interval

Answer

A separate query per category added as individual visualizations to the dashboard

Answer

A parameter default value set to a specific category in the second chart's query

Question 3

Which THREE actions occur when using Auto Loader with schema evolution enabled? (Choose three)

Accepted Answer

New columns found in the source files are automatically added to the target table.. Schema evolution is a powerful feature of Auto Loader that allows pipelines to adapt to changes in upstream data without requiring manual code updates. By understanding how the schema is managed, detected, and reconciled, analysts can build flexible ingestion systems that reduce maintenance overhead. This is critical in modern data environments where upstream sources frequently add or rename columns, requiring the platform to dynamically update downstream tables to accommodate the new structure.

Answer

Existing columns with data type conflicts cause the pipeline to stop immediately.

Answer

The schema is inferred from the metadata of the cloud storage provider.

Question 4

An analyst is configuring an AI/BI Genie Space and wants to ensure that the model only uses a specific SQL warehouse for query execution. Where should the analyst configure this setting?

Accepted Answer

In the Genie Space settings, under the SQL warehouse section.. To specify the SQL warehouse for a Genie Space, the analyst must use the Genie Space settings. There, they can select the desired warehouse from available options. This configuration ensures that all natural language queries are executed on the chosen warehouse, providing control over performance, cost, and permissions.

Answer

In the Databricks workspace admin console, under the Genie tab.

Answer

In the SQL query editor of the Genie Space, by adding a USE WAREHOUSE statement.

Answer

In the Unity Catalog metastore settings.

Question 5

An analyst is writing a query in the Databricks SQL editor and needs to reference a temporary view that was created earlier in the same interactive session. How are temporary views scoped within Databricks SQL environments?

Accepted Answer

They are scoped strictly to the current interactive session and drop automatically upon disconnection.. Temporary views in Databricks are scoped to the specific session or notebook in which they are defined. They disappear automatically once the session terminates, ensuring temporary scratchpads do not pollute global catalog namespaces while allowing complex multi-step queries within a single workflow.

Answer

They are globally visible to all users across the entire Databricks workspace indefinitely.

Answer

They persist permanently inside the default Hive metastore schema until manually dropped by an admin.

Answer

They are automatically replicated to all attached SQL warehouses for cross-warehouse analytics.

Question 6

A data analyst is exploring a large Delta table named sales in the Databricks SQL query editor. Before writing a detailed report, the analyst wants to quickly view the first 10 rows to understand the column names and data types. Which SQL statement should the analyst execute?

Accepted Answer

SELECT * FROM sales LIMIT 10;. The analyst needs to preview actual rows from the sales table to understand its structure and content. Using SELECT * with LIMIT 10 retrieves a small sample efficiently. Other options return metadata or invalid syntax, none of which provide the required row-level data. This approach is standard for initial data exploration in Databricks SQL.

Answer

SHOW TABLES IN default LIKE 'sales';

Answer

DESCRIBE EXTENDED sales;

Answer

SELECT * FROM sales WHERE row_number <= 10;

Question 7

An analyst is designing a dashboard in Databricks SQL to monitor key performance indicators (KPIs). The dashboard will be viewed by executives who need to quickly see the current value of each KPI and whether it is meeting its target. Which visualization type is most appropriate for displaying a single KPI value along with its target?

Accepted Answer

Counter visualization. The counter visualization is specifically designed for displaying a single KPI value with optional target comparison. It provides a clear, visual indication of whether the KPI meets its target, making it the best choice for executive dashboards that require quick status assessment.

Answer

Bar chart

Answer

Table

Answer

Pie chart

Question 8

When migrating a reporting workload to Databricks SQL, an analyst needs to ensure that specific query results are consistent across multiple runs, even if the underlying Delta table is being updated. Which feature should the analyst utilize?

Accepted Answer

Delta Lake Time Travel (VERSION AS OF / TIMESTAMP AS OF).. Time Travel allows analysts to query a table as it existed at a specific point in time or version. This is critical for audits and reproducibility, ensuring that reports generated today can be replicated exactly, even if new data is added or old data is deleted. Understanding how to use the 'AS OF' syntax is vital for maintaining high standards of data integrity in analytical reporting workflows within the Databricks environment.

Answer

Materialized Views.

Answer

Setting the table to 'Read-Only' during reports.

Answer

Database snapshots created via manual backups.

Question 9

A data analyst has a 5 MB pipe-delimited text file with a header row on their local laptop and needs to load it into a Databricks Unity Catalog table for a one-time analysis. They want the fastest path with the least configuration. Which approach should they use?

Accepted Answer

Use the Add Data UI to upload the file and create the table through the guided wizard.. For a small local file that needs to become a table once, the Add Data UI is the intended workflow: it uploads the file, previews it, lets the analyst confirm delimiter and header handling, and creates the table. The other approaches assume cloud storage paths or streaming infrastructure that a laptop file simply does not have.

Answer

Run COPY INTO against the local filesystem path of the downloaded file.

Answer

Configure an Auto Loader stream pointing at a cloud storage location to ingest the file.

Answer

Create an external table over the local file so the data stays in place.

Question 10

When querying a Delta table that is updated frequently, what is the most reliable way to ensure an analyst sees the most up-to-date data?

Accepted Answer

The Delta Lake engine automatically ensures read-consistency. Delta Lake maintains a transaction log that ensures ACID compliance. When a query is initiated, the engine reads the latest version of the transaction log to determine which data files are current. Because the transaction log is the source of truth, the engine automatically provides read-consistent snapshots, ensuring that the analyst always queries the current state of the table without needing manual cache refreshes.

Answer

Run REFRESH TABLE before every query

Answer

Clear the browser cache in the SQL editor

Answer

Execute the VACUUM command to force an update