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| Section | Weight | Objectives |
|---|---|---|
| Analyzing Queries | 15% | - Execution plans and analysis - Liquid clustering and indexing - Performance optimization - Query history and auditing |
| Securing Data | 8% | - Secure storage and compliance - Access control and permissions - Data governance policies |
| Importing Data | 5% | - API and Auto Loader - Databricks Marketplace - Delta Sharing - S3 and cloud storage integration - UI-based data ingestion |
| Creating Dashboards and Visualizations in Databricks | 16% | - Scheduling and sharing dashboards - Visualization types and best practices - Dashboard creation and layout - Filtering and interactivity |
| Data Modeling with Databricks SQL | 5% | - Schema design principles - Delta table structure - Performance-oriented modeling |
| Managing Data | 8% | - Data cleaning and preparation - Discovering and registering datasets - Unity Catalog usage - Dataset versioning and management |
| Executing Queries using Databricks SQL and Databricks SQL Warehouses | 20% | - Joining and combining datasets - ANSI SQL syntax and functions - Creating and managing views - Warehouse configuration and performance - Aggregations and grouping |
| Developing, Sharing, and Maintaining AI/BI Genie Spaces | 12% | - Natural language query setup - Genie space setup and configuration - Access control and sharing - Maintenance and improvement |
| Understanding of Databricks Data Intelligence Platform | 11% | - Core architecture and components - Lakehouse platform fundamentals - Workspace navigation and interface |
>> Databricks-Certified-Data-Analyst-Associate Latest Exam Answers <<
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NEW QUESTION # 50
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use.
Which of the following terms is used to describe this data augmentation?
Answer: E
Explanation:
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog
NEW QUESTION # 51
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?
Answer: D
NEW QUESTION # 52
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:
Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?
Answer: D
Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders
/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. References: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks
NEW QUESTION # 53
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: A
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 # 54
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?
Answer: D
Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE
NEW QUESTION # 55
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