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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Developing, Sharing, and Maintaining AI/BI Genie Spaces	12%	- Genie space setup and configuration - Maintenance and improvement - Access control and sharing - Natural language query setup
Securing Data	8%	- Secure storage and compliance - Access control and permissions - Data governance policies
Creating Dashboards and Visualizations in Databricks	16%	- Scheduling and sharing dashboards - Visualization types and best practices - Dashboard creation and layout - Filtering and interactivity
Understanding of Databricks Data Intelligence Platform	11%	- Workspace navigation and interface - Core architecture and components - Lakehouse platform fundamentals
Executing Queries using Databricks SQL and Databricks SQL Warehouses	20%	- Joining and combining datasets - ANSI SQL syntax and functions - Creating and managing views - Aggregations and grouping - Warehouse configuration and performance
Data Modeling with Databricks SQL	5%	- Delta table structure - Performance-oriented modeling - Schema design principles
Analyzing Queries	15%	- Liquid clustering and indexing - Performance optimization - Query history and auditing - Execution plans and analysis
Importing Data	5%	- UI-based data ingestion - Databricks Marketplace - Delta Sharing - S3 and cloud storage integration - API and Auto Loader
Managing Data	8%	- Dataset versioning and management - Unity Catalog usage - Discovering and registering datasets - Data cleaning and preparation

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As a substitute with less functionality
B. As an exact substitute with the same level of functionality
C. As a complementary tool for professional-grade presentations
D. As a complete replacement with additional functionality
E. As a complementary tool for quick in-platform Bl work

Answer: E

Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 65
Which of the following commands will return the location of database customer360?

A. DESCRIBE LOCATION customer360;
B. DESCRIBE DATABASE customer360;
C. DROP DATABASE customer360;
D. USE DATABASE customer360;
E. ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' );

Answer: B

Explanation:
Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema's "location on the filesystem."

NEW QUESTION # 66
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to successfully complete before the new task begins.
B. When another task needs to use as little compute resources as possible.
C. When another task needs to be replaced by the new task.
D. When another task has the same dependency libraries as the new task.
E. When another task needs to fail before the new task begins.

Answer: A

Explanation:
Option E is correct. The Depends On field is used to define task dependencies in a Databricks job, so a downstream task waits for an upstream task according to the dependency rules. The standard use case is when one task must complete successfully before the next task starts. Official Databricks extract: "You can control the execution order of tasks by specifying dependencies between them," and Databricks runs upstream tasks before downstream tasks.

NEW QUESTION # 67
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?

A. Last-mile ETL
B. Data testing
C. Last-mile
D. Data enhancement
E. Ad-hoc improvements

Answer: D

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 # 68
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can reduce the cluster size of the SQL endpoint.

Answer: B

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 69
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