Databricks Databricks-Certified-Data-Analyst-Associate Quizfragen Und Antworten & Databricks-Certified-Data-Analyst-Associate Online Praxisprüfung

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

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

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q28-Q33):

28. Frage
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

A. Computations, data, and analytical tools on the same platform
B. Automated alerting systems
C. Simultaneous multi-user support
D. Advanced dashboarding capabilities

Antwort: A

Begründung:
Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.
Reference: Databricks AI & BI: Transform Data into Actionable Insights

29. Frage
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

A. The table did not have a location
B. The table ' s data was smaller than 10 GB
C. The table ' s data was larger than 10 GB
D. The table was managed
E. The table was external

Antwort: E

Begründung:
An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. References: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table

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

Antwort: E

Begründung:
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.

31. Frage
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable Chat can take on a finite or countably infinite set of values
B. A categorical variable in which the number of categories continues to increase over time
C. A quantitative variable that never stops changing
D. A quantitative variable that can take on an uncountable set of values

Antwort: D

Begründung:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

32. Frage
A data analyst has written and saved a series of queries that reveal trends that need to be monitored by several stakeholders.
Which tool should the data analyst use to share the results of all of the queries to be viewed at once?

A. A SQL warehouse
B. A Query History page
C. A dashboard
D. A data visualization tab on a Query page

Antwort: C

Begründung:
The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.
Official documentation extract used: Databricks states that dashboards are used to "build data visualizations and share reports with your team."

33. Frage
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