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Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions – Free Practice Test & Real Exam Prep
Question #1
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a querywhen the query is not attached to a dashboard or alert?
A. Data bxplorer
B. The Visualization editor
C. The Query Editor
D. The Dashboard Editor
Answer: C
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or
alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up
scheduled executions for queries. This feature enables the query to run at specified intervals,
ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can
automate data refreshes and maintain up-to-date query results without manual intervention.
Reference: Schedule a query - Databricks Documentation
Question #2
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead ofusing third-party Bl tools?
A. Computations, data, and analytical tools on the same platform
B. Advanced dashboarding capabilities
C. Simultaneous multi-user support
D. Automated alerting systems
Answer: A
Databricks SQL offers a unified platform where computations, data storage, and analytical tools
coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed
more efficiently, as users can perform data processing and analysis without the need to transfer data
between disparate systems. By consolidating these components, Databricks SQL streamlines
workflows, reduces latency, and enhances data governance. While third-party BI tools may offer
advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting
systems, they often require integration with separate data processing platforms, which can introduce
complexity and potential inefficiencies.
Reference: Databricks AI & BI: Transform Data into Actionable Insights
Question #3
What describes the variance of a set of values?
A. Variance is a measure of how far a single observed value is from a set ot va IN
B. Variance is a measure of how far an observed value is from the variable's maximum or minimum
value.
C. Variance is a measure of central tendency of a set of values.
D. Variance is a measure of how far a set of values is spread out from the sets central value.
Answer: D
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around
their mean (central value). It is calculated by taking the average of the squared differences between
each value and the mean of the dataset. A higher variance indicates that the data points are more
spread out from the mean, while a lower variance suggests that they are closer to the mean. This
measure is fundamental in statistics to understand the degree of variability within a
dataset. WikipediaWikipedia+1Investopedia+1
Reference: Variance - Wikipedia
Question #4
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role inthe Databricks Lakehouse Platform use Databricks SQL as their primary service?
A. Data scientist
B. Data engineer
C. Platform architect
D. Business analyst
Answer: D
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main
service. Databricks SQL provides an environment tailored for executing SQL queries, creating
visualizations, and developing dashboards, which aligns with the typical responsibilities of business
analysts who focus on interpreting data to inform business decisions. While data scientists and data
engineers also interact with the Databricks platform, their primary tools and services differ; data
scientists often engage with machine learning frameworks and notebooks, whereas data engineers
focus on data pipelines and ETL processes. Platform architects are involved in designing and
overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed,
business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform
Question #5
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. Thedata analyst needs to upload this dataset for use as a table in Databricks SQL.Which approach should the data analyst use to quickly upload the file into a table for use inDatabricks SOL?
A. Create a table by uploading the file using the Create page within Databricks SQL
B. Create a table via a connection between Databricks and the desktop facilitated by PartnerConnect.
C. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
D. Create a table by manually copying and pasting the data values into cloud storage and thenimporting the data to Databricks.
Answer: A
Databricks provides a user-friendly interface that allows data analysts to quickly upload small
datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area.
The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as
needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or
complex configurations. Options B, C, and D involve more complex or manual processes that are
unnecessary for this task.
Reference: Create or modify a table using file upload
Question #6
What does Partner Connect do when connecting Power Bl and Tableau?
A. Creates a Personal Access Token. downloads and installs an ODBC driver, and downloads aconfiguration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as aSQL Endpoint).
B. Creates a Personal Access Token for authentication into Databricks SQL and emails it to you.
C. Downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse(formerly known as a SQL Endpoint)
D. Downloads and installs an ODBC driver.
Answer: A
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates
several steps to streamline the integration process:
Personal Access Token Creation: Partner Connect generates a Databricks personal access token,
which is essential for authenticating and establishing a secure connection between Databricks and
the BI tools.
ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver
facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal
performance.
Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or
Tableau) is provided. This file contains the necessary connection details, simplifying the setup
process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing
manual configuration efforts and potential errors.
Reference: Connect Tableau and Databricks
Question #7
A data engineering team has created a Structured Streaming pipeline that processes data in microbatchesand populates gold-level tables. The microbatches are triggered every 10 minutes.A data analyst has created a dashboard based on this gold level data. The project stakeholders want to see the results in the dashboard updated within 10 minutes orless of new data becoming available within the gold-level tables.What is the ability to ensure the streamed data is included in the dashboard at the standardrequested by the project stakeholders?
A. A refresh schedule with an interval of 10 minutes or less
B. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint
C. A refresh schedule with stakeholders included as subscribers
D. A refresh schedule with a Structured Streaming cluster
Answer: A
In this scenario, the data engineering team has configured a Structured Streaming pipeline that
updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most
recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less.
This synchronization ensures that stakeholders view the latest information shortly after it becomes
available in the gold-level tables. Options B, C, and D do not directly address the requirement of
aligning the dashboard refresh frequency with the data update interval.
Question #8
What describes Partner Connect in Databricks?
A. it allows for free use of Databricks partner tools through a common API.
B. it allows multi-directional connection between Databricks and Databricks partners easier.
C. It exposes connection information to third-party tools via Databricks partners.
D. It is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly knownas a SQL endpoint).
Answer: B
Databricks Partner Connect is designed to simplify and streamline the integration between
Databricks and its technology partners. It provides a unified interface within the Databricks platform
that facilitates the discovery and connection to a variety of data, analytics, and AI tools. By
automating the configuration of necessary resources such as clusters, tokens, and connection files,
Partner Connect enables seamless, bi-directional data flow between Databricks and partner
solutions. This integration enhances the overall functionality of the Databricks Lakehouse by allowing
users to easily incorporate external tools and services into their workflows, thereby expanding the
platform's capabilities and fostering a more cohesive data
B. Subqueries can be used like other user-defined functions to transform data into different datatypes.
C. Subqueries can retrieve data without requiring the creation of a table or view.
D. Subqueries can be used like other built-in functions to transform data into different data types.
Answer: C
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval
of data without the necessity of creating a table or view. This is particularly useful for simplifying
complex queries by breaking them down into more manageable parts. Subqueries can be employed
in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering,
transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and
readability without the overhead of persisting intermediate results as separate tables or views.
Reference: Databricks SQL Query Syntax
Question #10
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder hasprovided the analyst with an additional dataset that can be used to augment the gold-layer tablesalready in use.Which of the following terms is used to describe this data augmentation?
A. Data testing
B. Ad-hoc improvements
C. Last-mile
D. Last-mile ETL
E. Data enhancement
Answer: E
Data enhancement is the process of adding or enriching data with additional information to improve
its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data
sources with new data sources, such as external datasets, synthetic data, or machine learning
models. Data enhancement can help data analysts to gain deeper insights, discover new patterns,
and solve complex problems. Data enhancement is one of the applications of generative AI, which
can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated
business-level tables that are typically organized in consumption-ready project-specific
databases234. The gold-layer tables are the final layer of data transformations and data quality rules
in the medallion lakehouse architecture, which is a data design pattern used to logically organize
data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be
used to augment the gold-layer tables already in use. This means that the analyst can use the
additional dataset to enhance the existing gold-layer tables with more information, such as new
features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc
project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse
Platform | Databricks Blog
What is the medallion lakehouse architecture? - Azure Databricks
What is a Medallion Architecture? - Databricks
Synthetic Data for Better Machine Learning | Databricks Blog
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