Databricks-Certified-Data-Analyst-Associate Free Exam, Valid Dumps Databricks-Certified-Data-Analyst-Associate Questions

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

Section	Weight	Objectives
Topic 1: Securing Data	8%	- Data governance policies - Secure storage and compliance - Access control and permissions
Topic 2: Understanding of Databricks Data Intelligence Platform	11%	- Lakehouse platform fundamentals - Workspace navigation and interface - Core architecture and components
Topic 3: Managing Data	8%	- Discovering and registering datasets - Data cleaning and preparation - Dataset versioning and management - Unity Catalog usage
Topic 4: Developing, Sharing, and Maintaining AI/BI Genie Spaces	12%	- Genie space setup and configuration - Maintenance and improvement - Natural language query setup - Access control and sharing
Topic 5: Data Modeling with Databricks SQL	5%	- Delta table structure - Schema design principles - Performance-oriented modeling
Topic 6: Analyzing Queries	15%	- Performance optimization - Execution plans and analysis - Liquid clustering and indexing - Query history and auditing
Topic 7: Executing Queries using Databricks SQL and Databricks SQL Warehouses	20%	- Aggregations and grouping - ANSI SQL syntax and functions - Warehouse configuration and performance - Joining and combining datasets - Creating and managing views
Topic 8: Creating Dashboards and Visualizations in Databricks	16%	- Filtering and interactivity - Visualization types and best practices - Dashboard creation and layout - Scheduling and sharing dashboards
Topic 9: Importing Data	5%	- UI-based data ingestion - Databricks Marketplace - API and Auto Loader - Delta Sharing - S3 and cloud storage integration

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
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. spark.table( " sales " )
B. SELECT * FROM sales
C. spark.delta.table( " sales " )
D. spark.sql( " sales " )

Answer: A

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 # 36
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 substitute with less functionality
B. As a complete replacement with additional functionality
C. As a complementary tool for professional-grade presentations
D. As a complementary tool for quick in-platform Bl work
E. As an exact substitute with the same level of functionality

Answer: D

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 # 37
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DROP DATABASE database_name;
B. DELETE TABLE database_name.table_name;
C. DELETE TABLE table_name FROM database_name;
D. DROP TABLE database_name.table_name;
E. DROP TABLE table_name FROM database_name;

Answer: D

Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE

NEW QUESTION # 38
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
B. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
C. Running SELECT * FROM accounts.customers will return all rows in the table.
D. All files with the .customers extension are deleted.
E. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.

Answer: E

Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 39
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 command fails because it is written incorrectly.
B. The suppliers table now contains only the data from the new suppliers table.
C. 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.
D. 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.
E. 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.

Answer: A

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.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 40
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