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

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As a complete replacement with additional functionality
B. As a complementary tool for quick in-platform Bl work
C. As a substitute with less functionality
D. As an exact substitute with the same level of functionality
E. As a complementary tool for professional-grade presentations

Answer: B

Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 112
Which open-source project helps to enable the data lakehouse by adding organization, reliability, performance, and data governance to data lake architectures?

A. Apache Spark
B. Delta Lake
C. MLflow
D. Databricks SQL

Answer: B

Explanation:
The correct answer is C because Delta Lake is the open-source storage layer that provides reliability and structure for data lakes. Delta Lake brings ACID transactions, scalable metadata handling, and batch
/streaming support to data lake storage, which are core capabilities behind the lakehouse architecture. MLflow is for machine learning lifecycle management, Apache Spark is a distributed processing engine, and Databricks SQL is a Databricks product for SQL analytics, not the open-source storage project described.
Official documentation extract used: Databricks states that Delta Lake is "open source software" and provides
"ACID transactions and scalable metadata handling."

NEW QUESTION # 113
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?

A. Colors can be changed.
B. Tooltips can be formatted.
C. Borders can be added.
D. Visualization scale can be changed.
E. Data Labels can be formatted.

Answer: C

Explanation:
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. Reference:
New chart visualizations in Databricks | Databricks on AWS

NEW QUESTION # 114
Consider the following two statements:
Statement 1:

Statement 2:
Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

A. Both statements will fail because Databricks SQL does not support those join types.
B. The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
C. When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
D. There is no difference between the result sets for both statements.
E. When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

Answer: C

Explanation:
Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id).
Therefore, the result sets for the two statements will differ in the following way:
* The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.
* The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.
The other options are not correct because:
* A. The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.
* C. There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.
* D. Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.
* E. The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.
References: JOIN | Databricks on AWS, JOIN - Azure Databricks - Databricks SQL | Microsoft Learn, array_join function | Databricks on AWS, Hints | Databricks on AWS

NEW QUESTION # 115
Which of the following data workloads will utilize a Gold table as its source?

A. A job that aggregates uncleaned data to create standard summary statistics.
B. A job that ingests raw data from a streaming source into the Lakehouse.
C. A job that enriches data by parsing its timestamps into a human-readable format.
D. A job that cleans data by removing malformatted records.
E. A job that queries aggregated data designed to feed into a dashboard.

Answer: E

Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

NEW QUESTION # 116
......

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