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

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

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q20-Q25):

20. Frage
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be applied to simple, unnested data
B. When custom logic needs to be converted to Python-native code
C. When custom logic needs to be applied at scale to array data objects
D. When built-in functions are taking too long to perform tasks
E. When built-in functions need to run through the Catalyst Optimizer

Antwort: C

Begründung:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

21. Frage
A data analyst at an e-commerce company needs to process daily sales data. The data consists of approximately 50,000 records stored in a single CSV file, totaling about 20 MB. The analyst needs to perform aggregations and generate a summary report.
Which approach could the data analyst use in this situation?

A. Deploy a real-time streaming solution using Spark Streaming to process incoming data.
B. Implement Apache Spark with a distributed cluster to process the data in parallel.
C. Set up a Hadoop ecosystem with HDFS and MapReduce for distributed processing.
D. Use a local Python script with the pandas library to read and analyze the CSV file.

Antwort: D

Begründung:
Option B is correct. A 20 MB CSV file with about 50,000 records is small enough for a local pandas workflow. A real-time streaming solution, a distributed Spark cluster, or Hadoop MapReduce would add unnecessary complexity for a small single-file batch analysis. Databricks documentation explains that pandas is available in Databricks Runtime and can be used for data analysis, while pandas API on Spark is useful when pandas-style workloads need to scale beyond smaller datasets. Databricks also notes that pandas does not scale out to big data, which is exactly why Spark or pandas API on Spark is used for larger workloads, not for a small 20 MB file.

22. Frage
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

A. Edit the Owner field in the table page by removing their own account
B. Edit the Owner field in the table page by removing all access
C. Edit the Owner field in the table page by selecting the new owner ' s account
D. Edit the Owner field in the table page by selecting the Admins group
E. Edit the Owner field in the table page by selecting All Users

Antwort: C

Begründung:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
* A. Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
* B. Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
* D. Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
* E. Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. References:
* 1: Change table ownership
* 2: Ownerless tables
* 3: Table access control
* 4: Revoke access to a table

23. Frage
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production.
They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

A. Separate color palettes for each section
B. Markdown-based text boxes
C. Separate queries for each section
D. Direct text written into the dashboard in editing mode
E. Separate endpoints for each section

Antwort: B

Begründung:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. References: Dashboards in notebooks, How to add text to a dashboard in Databricks

24. Frage
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
B. A markdown-based tile must be added to the top of the dashboard displaying the dashboard ' s name.
C. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
D. All data visualizations and counters must be created using Queries.

Antwort: D

Begründung:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
* Develop Queries:
* For each desired visualization or counter, write a SQL query that retrieves the necessary data.
* Create Visualizations and Counters:
* After executing each query, utilize the results to create corresponding visualizations or counters.
Databricks SQL offers a variety of visualization types to represent data effectively.
* Assemble the Dashboard:
* Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.
Reference: Visualization in Databricks SQL

25. Frage
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