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

Topic	Details
Topic 1	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 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	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 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	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 (Q20-Q25):

NEW QUESTION # 20
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

A. The table was managed
B. The table was external
C. The table's data was larger than 10 GB
D. The table's data was smaller than 10 GB
E. The table did not have a location

Answer: B

Explanation:
An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. Reference: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table

NEW QUESTION # 21
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses " Dashboard Parameter " for the query parameter associated with the area chart.
Which of the following statements is true?

A. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
B. The area chart will convert to a Dashboard Parameter.
C. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
D. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.
E. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.

Answer: A

Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. References: Databricks SQL dashboards, Query parameters

NEW QUESTION # 22
A data analyst has written and saved a series of queries that reveal trends that need to be monitored by several stakeholders.
Which tool should the data analyst use to share the results of all of the queries to be viewed at once?

A. A SQL warehouse
B. A dashboard
C. A Query History page
D. A data visualization tab on a Query page

Answer: B

Explanation:
The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.
Official documentation extract used: Databricks states that dashboards are used to "build data visualizations and share reports with your team."

NEW QUESTION # 23
A data analyst wants to generate insights from large, complex datasets. The analyst needs to quickly understand the meaning of various data columns, ask questions in natural language, and receive AI-driven recommendations for optimizing data queries and workflows.
Which Databricks component is primarily responsible for enabling these capabilities?

A. Data Intelligence Engine
B. Unity Catalog
C. Databricks Assistant
D. Genie Spaces

Answer: A

Explanation:
Option A is correct. The Data Intelligence Engine is the platform-level intelligence layer that understands the semantics and uniqueness of an organization's data and enables AI-assisted experiences across Databricks.
Unity Catalog provides governance and metadata management, Genie Spaces provide a natural-language interface for curated business data, and Databricks Assistant is a user-facing assistant for code/query help.
However, the question asks which component is primarily responsible for enabling these capabilities across the platform; that is the Data Intelligence Engine. Databricks describes the platform as powered by a Data Intelligence Engine that understands the uniqueness and semantics of data and helps optimize performance.
References: Databricks Data Intelligence Platform documentation and Data Analyst Associate Exam Guide.

NEW QUESTION # 24
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which command could the data engineering team use to access sales in PySpark?

A. SELECT * FROM sales
B. spark.table( " sales " )
C. spark.sql( " sales " )
D. spark.delta.table( " sales " )

Answer: B

Explanation:
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.

NEW QUESTION # 25
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