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| Section | Weight | Objectives |
|---|---|---|
| Understanding of Databricks Data Intelligence Platform | 11% | - Core architecture and components - Lakehouse platform fundamentals - Workspace navigation and interface |
| Importing Data | 5% | - API and Auto Loader - UI-based data ingestion - Delta Sharing - S3 and cloud storage integration - Databricks Marketplace |
| Analyzing Queries | 15% | - Execution plans and analysis - Liquid clustering and indexing - Query history and auditing - Performance optimization |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Natural language query setup - Maintenance and improvement - Genie space setup and configuration - Access control and sharing |
| Managing Data | 8% | - Unity Catalog usage - Data cleaning and preparation - Discovering and registering datasets - Dataset versioning and management |
| Securing Data | 8% | - Secure storage and compliance - Access control and permissions - Data governance policies |
| Creating Dashboards and Visualizations in Databricks | 16% | - Dashboard creation and layout - Scheduling and sharing dashboards - Filtering and interactivity - Visualization types and best practices |
| Data Modeling with Databricks SQL | 5% | - Delta table structure - Schema design principles - Performance-oriented modeling |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - ANSI SQL syntax and functions - Aggregations and grouping - Joining and combining datasets - Creating and managing views - Warehouse configuration and performance |
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NEW QUESTION # 57
In which of the following situations should a data analyst use higher-order functions?
Answer: B
Explanation:
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. References: 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
NEW QUESTION # 58
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?
Answer: D
Explanation:
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.
NEW QUESTION # 59
A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?
Answer: D
NEW QUESTION # 60
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the " Add visualization " button on the SQL Editor page, which of the following types of visualizations will be selected by default?
Answer: B
Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. References: Visualization in Databricks SQL, Visualization types
NEW QUESTION # 61
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?
Answer: A
Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area.
The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.
NEW QUESTION # 62
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