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| Section | Weight | Objectives |
|---|---|---|
| Importing Data | 5% | - API and Auto Loader - Delta Sharing - UI-based data ingestion - S3 and cloud storage integration - Databricks Marketplace |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Creating and managing views - ANSI SQL syntax and functions - Warehouse configuration and performance - Joining and combining datasets - Aggregations and grouping |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Maintenance and improvement - Access control and sharing - Natural language query setup - Genie space setup and configuration |
| Data Modeling with Databricks SQL | 5% | - Delta table structure - Schema design principles - Performance-oriented modeling |
| Managing Data | 8% | - Dataset versioning and management - Data cleaning and preparation - Unity Catalog usage - Discovering and registering datasets |
| Creating Dashboards and Visualizations in Databricks | 16% | - Scheduling and sharing dashboards - Dashboard creation and layout - Filtering and interactivity - Visualization types and best practices |
| Analyzing Queries | 15% | - Liquid clustering and indexing - Execution plans and analysis - Performance optimization - Query history and auditing |
| Understanding of Databricks Data Intelligence Platform | 11% | - Lakehouse platform fundamentals - Workspace navigation and interface - Core architecture and components |
| Securing Data | 8% | - Data governance policies - Access control and permissions - Secure storage and compliance |
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NEW QUESTION # 106
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: B
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 # 107
A data analyst has produced a visualization. A stakeholder has viewed the visualization and is complaining that the visualization is difficult to interpret. After looking at the visualization, the analyst determines that the scale of the y-axis must be changed.
Where are the controls for changing the scale of the y-axis in Databricks SQL?
Answer: D
Explanation:
The correct answer is C because axis settings are configured in the visualization editor, specifically under the Y-axis settings. The Query Editor is for writing SQL, not configuring chart axes. User Settings do not control individual chart scales. Dashboard editing may contain visualizations, but the specific control is in the visualization editor's Y Axis configuration.
Official documentation extract used: Databricks chart options state that to configure formatting for the X axis or Y axis, click the corresponding axis tab, and the Y-axis supports scale and range controls.
NEW QUESTION # 108
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?
Answer: C
Explanation:
Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: "You can increase the maximum clusters if you want to handle more concurrent users," and Databricks recommends monitoring queued queries and adjusting maximum clusters.
NEW QUESTION # 109
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?
Answer: A
Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide
NEW QUESTION # 110
Which of the following approaches can be used to ingest data directly from cloud-based object storage?
Answer: C
Explanation:
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.
NEW QUESTION # 111
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