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| Section | Weight | Objectives |
|---|---|---|
| Analyzing Queries | 15% | - Execution plans and analysis - Performance optimization - Query history and auditing - Liquid clustering and indexing |
| Securing Data | 8% | - Data governance policies - Secure storage and compliance - Access control and permissions |
| Data Modeling with Databricks SQL | 5% | - Performance-oriented modeling - Delta table structure - Schema design principles |
| Understanding of Databricks Data Intelligence Platform | 11% | - Workspace navigation and interface - Core architecture and components - Lakehouse platform fundamentals |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Access control and sharing - Natural language query setup - Maintenance and improvement - Genie space setup and configuration |
| Importing Data | 5% | - Databricks Marketplace - Delta Sharing - UI-based data ingestion - API and Auto Loader - S3 and cloud storage integration |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Creating and managing views - Warehouse configuration and performance - Aggregations and grouping - ANSI SQL syntax and functions - Joining and combining datasets |
| Creating Dashboards and Visualizations in Databricks | 16% | - Dashboard creation and layout - Visualization types and best practices - Scheduling and sharing dashboards - Filtering and interactivity |
| Managing Data | 8% | - Unity Catalog usage - Dataset versioning and management - Discovering and registering datasets - Data cleaning and preparation |
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NEW QUESTION # 104
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?
Answer: D
Explanation:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.
NEW QUESTION # 105
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?
Answer: A
Explanation:
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up- to-date query results without manual intervention.
Reference: Schedule a query - Databricks Documentation
NEW QUESTION # 106
Which of the following statements describes descriptive statistics?
Answer: E
Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data.
Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. References: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL
NEW QUESTION # 107
What is used as a compute resource for Databricks SQL?
Answer: C
NEW QUESTION # 108
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?
Answer: A
Explanation:
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. Reference:
Databricks SQL
Query data using SQL Analytics
Visualizations in Databricks notebooks
Delta Lake
NEW QUESTION # 109
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