Free Databricks Databricks-Certified-Data-Analyst-Associate Practice Exam with Questions & Answers | Set: 2

Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. References:

Databricks SQL

Query data using SQL Analytics

Visualizations in Databricks notebooks

Delta Lake

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.

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.​

A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster. References: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters

The SQL Editor page is where the analyst can write and execute SQL queries in Databricks SQL. The SQL Editor page has a query pane where the analyst can type or paste SQL statements, and a results pane where the analyst can view the query results in a table or a chart. The analyst can also browse data objects, edit multiple queries, execute a single query or multiple queries, terminate a query, save a query, download a query result, and more from the SQL Editor page. References: Create a query in SQL editor

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 onNew>Add or upload data.​

SelectCreate or modify a table.​

Upload the CSV File:

Click on thebrowsebutton 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 theCreate Tablebutton 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.​

 The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. References:

New chart visualizations in Databricks | Databricks on AWS

This statement isincorrect. In Databricks SQL,any user with sufficient permissionson the query or dashboard can configure arefresh 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 theQuery Editor.

C. False statement– A query being refresheddoes usea SQL Warehouse. However, the option in question says itdoes not usea 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)– Youdo notneed to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.

Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.