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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
Topic 2	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 3	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 4	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 5	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q110-Q115):

NEW QUESTION # 110
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production.
They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

A. Separate queries for each section
B. Separate endpoints for each section
C. Direct text written into the dashboard in editing mode
D. Markdown-based text boxes
E. Separate color palettes for each section

Answer: D

Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. References: Dashboards in notebooks, How to add text to a dashboard in Databricks

NEW QUESTION # 111
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 a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL 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

Explanation:
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

NEW QUESTION # 112
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can reduce the cluster size of the SQL endpoint.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.

Answer: B

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 113
Which statement about subqueries is correct?

A. Subqueries can be used like other built-in functions to transform data into different data types.
B. Subqueries can retrieve data without requiring the creation of a table or view.
C. Subqueries are not available in Databricks SQL
D. Subqueries can be used like other user-defined functions to transform data into different data types.

Answer: B

Explanation:
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

NEW QUESTION # 114
Which statement about subqueries is correct?

A. Subqueries can be used like other built-in functions to transform data into different data types.
B. Subqueries can retrieve data without requiring the creation of a table or view.
C. Subqueries are not available in Databricks SQL
D. Subqueries can be used like other user-defined functions to transform data into different data types.

Answer: B

Explanation:
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.

NEW QUESTION # 115
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