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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Analyst Associate Exam |
| Exam Number: | Databricks-Certified-Data-Analyst-Associate |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Real Exam Qty: | 45–60 |
| Exam Format: | Multiple Select, Multiple Choice |
| Exam Price: | $200 USD |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Passing Score: | 70% |
| Recommended Training: | Databricks SQL Training Courses Databricks Academy - Data Analyst Learning Path |
| Exam Registration: | Kryterion Webassessor Registration Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions |
| Exam Way: | Online proctored exam via remote monitoring (Kryterion Webassessor platform) |
| Pre Condition: | No formal prerequisites required, but familiarity with SQL and basic data analysis concepts is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 34
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?
Answer: C
Explanation:
The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. References:
* New chart visualizations in Databricks | Databricks on AWS
NEW QUESTION # 35
What is an advantage of using a Delta Lake-based data lakehouse over classic enterprise data warehouse solutions?
Answer: C
Explanation:
Option A is correct. Classic enterprise data warehouses commonly use proprietary storage formats, while the Databricks lakehouse is built around open table formats such as Delta Lake and Iceberg. Databricks documentation states that Delta Lake is open source and that the Databricks platform uses no proprietary data formats to avoid vendor lock-in. Schema enforcement and ACID transactions are important Delta Lake capabilities, but they are not the best differentiator "over classic enterprise data warehouse solutions," because enterprise warehouses also typically provide managed reliability and transactional behavior. The unique lakehouse advantage here is open-source/open-format interoperability. References: Databricks Delta Lake and lakehouse architecture documentation.
NEW QUESTION # 36
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?
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. References: Databricks - DROP TABLE
NEW QUESTION # 37
Which statement about subqueries is correct?
Answer: A
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 # 38
What does Partner Connect do when connecting Power Bl and Tableau?
Answer: C
Explanation:
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:
Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.
ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.
Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.
NEW QUESTION # 39
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