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| Section | Weight | Objectives |
|---|---|---|
| Creating Dashboards and Visualizations in Databricks | 16% | - Scheduling and sharing dashboards - Dashboard creation and layout - Filtering and interactivity - Visualization types and best practices |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Creating and managing views - Joining and combining datasets - Warehouse configuration and performance - Aggregations and grouping - ANSI SQL syntax and functions |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Genie space setup and configuration - Access control and sharing - Maintenance and improvement - Natural language query setup |
| Managing Data | 8% | - Unity Catalog usage - Dataset versioning and management - Data cleaning and preparation - Discovering and registering datasets |
| Securing Data | 8% | - Secure storage and compliance - Data governance policies - Access control and permissions |
| Data Modeling with Databricks SQL | 5% | - Schema design principles - Performance-oriented modeling - Delta table structure |
| Importing Data | 5% | - UI-based data ingestion - Delta Sharing - S3 and cloud storage integration - API and Auto Loader - Databricks Marketplace |
| Analyzing Queries | 15% | - Liquid clustering and indexing - Performance optimization - Query history and auditing - Execution plans and analysis |
| Understanding of Databricks Data Intelligence Platform | 11% | - Workspace navigation and interface - Lakehouse platform fundamentals - Core architecture and components |
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NEW QUESTION # 114
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: A
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 # 115
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?
Answer: A
Explanation:
Option E is correct. A major benefit of open source technologies and open data formats is avoiding vendor lock-in. Databricks supports open formats and interfaces so data can be used across tools and systems rather than being locked into a proprietary platform. Cloud integrations, governance, and workload scalability are Databricks benefits, but the specific benefit of embracing open source is avoiding vendor lock-in. Official Databricks extract: "Using open data formats and interfaces helps to avoid" vendor lock-in, and Databricks states that no proprietary data formats are used because Delta Lake and Iceberg are open source.
NEW QUESTION # 116
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: D
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 # 117
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?
Answer: E
Explanation:
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
NEW QUESTION # 118
Which of the following layers of the medallion architecture is most commonly used by data analysts?
Answer: C
Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. References: What is the medallion lakehouse architecture?
NEW QUESTION # 119
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