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

Section	Weight	Objectives
Topic 1: Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Reading Query Plans 3. Performance Tuning
Topic 2: Importing Data	5%	- Data Import 1. Uploading Files 2. Creating Tables from Imported Data 3. Loading CSV and JSON Data
Topic 3: Securing Data	8%	- Security and Access Control 1. Permissions and Roles 2. Data Access Policies 3. Unity Catalog Security
Topic 4: Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Data Organization 2. Views and Relationships 3. Fact and Dimension Tables
Topic 5: Creating Dashboards and Visualizations	16%	- Visualization 1. Charts and Graphs 2. Sharing and Publishing 3. Dashboards 4. Alerts and Filters
Topic 6: Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Workspace Configuration 2. Natural Language Queries 3. AI/BI Genie
Topic 7: Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. SQL Warehouses 2. Query Optimization 3. Filtering and Aggregation 4. Joins and Subqueries 5. Writing SQL Queries
Topic 8: Managing Data	8%	- Data Management 1. Tagging and Lineage 2. Discovering Data Assets 3. Handling Missing and Invalid Data 4. Cleaning Data with SQL
Topic 9: Understanding of Databricks Data Intelligence Platform	11%	- Platform Components 1. Databricks SQL 2. Unity Catalog 3. Delta Lake 4. Lakeflow Jobs 5. Mosaic AI - Catalog and Governance 1. Views and Certified Tables 2. Managed and External Tables 3. Data Lineage 4. Catalogs and Schemas

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
B. Use Delta Live Tables to establish a cluster for Fivetran to interact with
C. Use Partner Connect ' s automated workflow to establish a cluster for Fivetran to interact with
D. Use Workflows to establish a cluster for Fivetran to interact with
E. Use Partner Connect ' s automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

Answer: C

Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. References: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

NEW QUESTION # 41
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
B. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
C. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
D. The view has been converted into a table.
E. The view has been dropped.

Answer: B

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
* Views do not store data themselves but reference data from underlying tables.
* Logging out or being inactive does not delete or alter views.
* Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
* Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.
eur_customers, and it will return the same data (provided the underlying table hasn't changed).
Reference: Views - Databricks Documentation

NEW QUESTION # 42
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

Answer: B

Explanation:
Option D is correct because the table has zero real null values, but the result contains null values representing subtotal and grand-total rows. That behavior is produced by WITH CUBE, which creates aggregations for combinations of grouping columns, including (group_1, group_2), (group_1), (group_2), and the grand total ().
The Databricks SQL documentation states that GROUP BY supports advanced aggregations through CUBE, and that CUBE is shorthand for grouping sets. Option A only returns detailed groups. Options B and C use invalid syntax in Databricks SQL. Reference: Databricks GROUP BY clause documentation.

NEW QUESTION # 43
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 are not available in Databricks SQL
C. Subqueries can be used like other user-defined functions to transform data into different data types.
D. Subqueries can retrieve data without requiring the creation of a table or view.

Answer: D

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 # 44
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 submit a feature request with Databricks to add this functionality.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could only run the entire program on Sundays.
D. They could redesign the data model to separate the data used in the final query into a new table.
E. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.

Answer: E

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 # 45
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