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

Topic	Details
Topic 1	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 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	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 (Q52-Q57):

NEW QUESTION # 52
Which of the following statements about a refresh schedule is incorrect?

A. A query can be refreshed anywhere from 1 minute lo 2 weeks
B. Refresh schedules can be configured in the Query Editor.
C. A refresh schedule is not the same as an alert.
D. You must have workspace administrator privileges to configure a refresh schedule
E. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).

Answer: E

Explanation:
Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.

NEW QUESTION # 53
A data analyst has two data sources that are providing similar but complementary information. The analyst wants to combine these sources of data into a single, comprehensive dataset for ongoing use for their team in a variety of different projects.
Which term is used to describe this type of work?

A. Ad-hoc improvements
B. Last-mile ETL
C. Data testing
D. Data blending

Answer: D

Explanation:
Option D is correct. The scenario describes combining multiple complementary data sources into one comprehensive dataset. That is data blending. Last-mile ETL is usually project-specific final transformation near the end of an analytics workflow, while this question emphasizes combining two source datasets for broader ongoing team use. The current official Databricks exam guide describes the same kind of capability as creating unified datasets by joining data from multiple sources. That aligns with the concept of data blending. Reference: Databricks Certified Data Analyst Associate Exam Guide.

NEW QUESTION # 54
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Business intelligence analyst
B. Business analyst
C. Data engineer
D. SQL analyst
E. Data analyst

Answer: C

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. References: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 55
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
B. The command fails because it is written incorrectly.
C. The suppliers table now contains only the data from the new suppliers table.
D. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
E. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.

Answer: B

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
* INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [,
...] ) | BY NAME ] query
* INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query
The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted.
The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 56
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
B. They could redesign the data model to separate the data used in the final query into a new table.
C. They could submit a feature request with Databricks to add this functionality.
D. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
E. They could only run the entire program on Sundays.

Answer: D

Explanation:
Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured "to run it on a time-based schedule," and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

NEW QUESTION # 57
......

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