Free Practice Questions for Databricks Databricks-Certified-Data-Analyst-Associate Exam

The correct answer is C because the tag key is confidential, and Databricks workspace search uses the syntax tag: < tag_key > to search by tag key. Therefore, the correct search text is tag:confidential. Option A uses a Boolean-style syntax that is not the documented Databricks tag search syntax. Option B uses an equals sign, and Option D is not valid workspace search syntax.

Official documentation extract used: Databricks states that to search by tag key alone, use the syntax tag: < tag_key > .

Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. References: Discover and manage data using Data Explorer

Option B is correct because spark.table( " sales " ) returns the named table as a Spark DataFrame, which the data engineering team can then test using PySpark. SELECT * FROM sales is SQL text, not a PySpark command by itself. spark.sql( " sales " ) is invalid because spark.sql expects a SQL statement, not just a table name. spark.delta.table is not the standard PySpark API for loading a table. Official Databricks extract: DataFrameReader.table “returns the specified table as a DataFrame,” and Databricks also lists spark.table as a Spark operation that returns a DataFrame.

Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks’ documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.

Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. References: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

 Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. References: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL

Option B is correct. In the older Databricks SQL UI terminology used by this question, permissions for databases, tables, and views are managed from the Data area. In the current Databricks UI, this is handled through Catalog Explorer, where an admin or data owner opens the object, goes to the Permissions tab, clicks Grant, selects users or groups, and grants the required privileges. Dashboards are for reporting, SQL Warehouses provide compute, and Settings is not the object-level permission management area. Official Databricks documentation says privileges can be managed using SQL commands, CLI, Terraform, or Catalog Explorer, and the UI flow is: open the table details page, go to Permissions, click Grant, select users or groups, and assign privileges.

This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule—workspace administrator privileges are not required.

Here is the breakdown of the correct information:

A. True – Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.

B. True – You can configure refresh schedules in the Query Editor.

C. False statement – A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.

D. True – Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.

E. False (and thus the correct answer to this question) – You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.

 Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. References:

Databricks SQL overview

Databricks SQL use cases

Joining data sources

The mean is sensitive to extreme values, often called outliers, which can significantly skew the average away from the true center of the data. The median, however, is a measure of central tendency that is resistant to such outliers because it only considers the middle value(s) when the data is ordered. Therefore, when a variable contains many extreme outliers, there will be a substantial difference between the mean and the median. According to Databricks data analysis materials, this is a fundamental concept when choosing summary statistics for reporting.