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| Section | Weight | Objectives |
|---|---|---|
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - ANSI SQL syntax and functions - Warehouse configuration and performance - Joining and combining datasets - Aggregations and grouping - Creating and managing views |
| Creating Dashboards and Visualizations in Databricks | 16% | - Filtering and interactivity - Dashboard creation and layout - Visualization types and best practices - Scheduling and sharing dashboards |
| Data Modeling with Databricks SQL | 5% | - Delta table structure - Performance-oriented modeling - Schema design principles |
| Understanding of Databricks Data Intelligence Platform | 11% | - Workspace navigation and interface - Lakehouse platform fundamentals - Core architecture and components |
| Analyzing Queries | 15% | - Performance optimization - Execution plans and analysis - Query history and auditing - Liquid clustering and indexing |
| Securing Data | 8% | - Secure storage and compliance - Access control and permissions - Data governance policies |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Maintenance and improvement - Access control and sharing - Genie space setup and configuration - Natural language query setup |
| Managing Data | 8% | - Discovering and registering datasets - Data cleaning and preparation - Dataset versioning and management - Unity Catalog usage |
| Importing Data | 5% | - S3 and cloud storage integration - Databricks Marketplace - API and Auto Loader - Delta Sharing - UI-based data ingestion |
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NEW QUESTION # 50
Which of the following statements about a refresh schedule is incorrect?
Answer: D
Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.
NEW QUESTION # 51
Which statement about subqueries is correct?
Answer: D
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 # 52
Which of the following statements about a refresh schedule is incorrect?
Answer: C
Explanation:
Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.
NEW QUESTION # 53
Which of the following statements describes descriptive statistics?
Answer: A
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. Reference: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL
NEW QUESTION # 54
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?
Answer: C
Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL
NEW QUESTION # 55
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