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

Section	Weight	Objectives
Analyzing Queries	15%	- Query Analysis 1. Performance Tuning 2. Monitoring Query Execution 3. Reading Query Plans
Managing Data	8%	- Data Management 1. Handling Missing and Invalid Data 2. Cleaning Data with SQL 3. Tagging and Lineage 4. Discovering Data Assets
Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Filtering and Aggregation 2. SQL Warehouses 3. Joins and Subqueries 4. Writing SQL Queries 5. Query Optimization
Creating Dashboards and Visualizations	16%	- Visualization 1. Alerts and Filters 2. Dashboards 3. Sharing and Publishing 4. Charts and Graphs
Securing Data	8%	- Security and Access Control 1. Data Access Policies 2. Permissions and Roles 3. Unity Catalog Security
Importing Data	5%	- Data Import 1. Loading CSV and JSON Data 2. Creating Tables from Imported Data 3. Uploading Files
Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Natural Language Queries 2. Workspace Configuration 3. AI/BI Genie
Understanding of Databricks Data Intelligence Platform	11%	- Catalog and Governance 1. Catalogs and Schemas 2. Data Lineage 3. Views and Certified Tables 4. Managed and External Tables - Platform Components 1. Mosaic AI 2. Databricks SQL 3. Lakeflow Jobs 4. Unity Catalog 5. Delta Lake
Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Fact and Dimension Tables 2. Data Organization 3. Views and Relationships

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

A. Data bxplorer
B. The Dashboard Editor
C. The Query Editor
D. The Visualization editor

Answer: C

Explanation:
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up-to-date query results without manual intervention.

NEW QUESTION # 72
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to fail before the new task begins.
B. When another task needs to be replaced by the new task.
C. When another task has the same dependency libraries as the new task.
D. When another task needs to use as little compute resources as possible.
E. When another task needs to successfully complete before the new task begins.

Answer: E

Explanation:
Option E is correct. The Depends On field is used to define task dependencies in a Databricks job, so a downstream task waits for an upstream task according to the dependency rules. The standard use case is when one task must complete successfully before the next task starts. Official Databricks extract: "You can control the execution order of tasks by specifying dependencies between them," and Databricks runs upstream tasks before downstream tasks.

NEW QUESTION # 73
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. 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.
B. There is no difference between the result sets for both statements.
C. 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.
D. 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.
E. Both statements will fail because Databricks SQL does not support those join types.

Answer: A

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.

NEW QUESTION # 74
A data analyst has developed a query that runs against a Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

A. spark.table
B. spark.sql
C. spark.delta.table
D. SELECT * FROM sales
E. There is no way to share data between PySpark and SQL.

Answer: B

Explanation:
Option C is correct because the requirement is to run the SQL query and then operate on the results in PySpark. spark.sql(...) executes SQL text and returns a Spark DataFrame that can be used in PySpark tests.
spark.table(...) can load a table directly, but it does not run an arbitrary SQL query. Option A is SQL text by itself, not a PySpark operation. Official Databricks documentation states that a SparkSession can be used to execute SQL over tables, and Databricks notebook documentation specifically references using SQL inside Python with a spark.sql command.

NEW QUESTION # 75
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;

A. Option A
B. Option B
C. Option E
D. Option C
E. Option D

Answer: A

Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement.
Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. References: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows

NEW QUESTION # 76
......

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