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

Section	Weight	Objectives
Topic 1: Creating Dashboards and Visualizations	16%	- Visualization 1. Charts and Graphs 2. Alerts and Filters 3. Dashboards 4. Sharing and Publishing
Topic 2: Understanding of Databricks Data Intelligence Platform	11%	- Platform Components 1. Lakeflow Jobs 2. Delta Lake 3. Mosaic AI 4. Databricks SQL 5. Unity Catalog - Catalog and Governance 1. Managed and External Tables 2. Data Lineage 3. Catalogs and Schemas 4. Views and Certified Tables
Topic 3: Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Data Organization 2. Views and Relationships 3. Fact and Dimension Tables
Topic 4: Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Query Optimization 2. Joins and Subqueries 3. Filtering and Aggregation 4. SQL Warehouses 5. Writing SQL Queries
Topic 5: Managing Data	8%	- Data Management 1. Discovering Data Assets 2. Tagging and Lineage 3. Cleaning Data with SQL 4. Handling Missing and Invalid Data
Topic 6: Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Performance Tuning 3. Reading Query Plans
Topic 7: Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Workspace Configuration 2. Natural Language Queries 3. AI/BI Genie
Topic 8: Importing Data	5%	- Data Import 1. Creating Tables from Imported Data 2. Uploading Files 3. Loading CSV and JSON Data
Topic 9: Securing Data	8%	- Security and Access Control 1. Data Access Policies 2. Permissions and Roles 3. Unity Catalog Security

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?

A. Segmenting customers into like groups using a clustering algorithm
B. Testing the quality of data as it is imported from a source
C. Tracking usage of feature variables for machine learning projects
D. Automating complex notebook-based workflows with multiple tasks
E. Combining two data sources into a single, comprehensive dataset

Answer: E

Explanation:
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. Reference:
Databricks SQL overview
Databricks SQL use cases
Joining data sources

NEW QUESTION # 74
A data analyst has recently joined a new team that uses Databricks SQL, but the analyst has never used Databricks before. The analyst wants to know where in Databricks SQL they can write and execute SQL queries.
On which of the following pages can the analyst write and execute SQL queries?

A. Alerts page
B. SQL Editor page
C. Dashboards page
D. Queries page
E. Data page

Answer: B

Explanation:
The SQL Editor page is where the analyst can write and execute SQL queries in Databricks SQL. The SQL Editor page has a query pane where the analyst can type or paste SQL statements, and a results pane where the analyst can view the query results in a table or a chart. The analyst can also browse data objects, edit multiple queries, execute a single query or multiple queries, terminate a query, save a query, download a query result, and more from the SQL Editor page. Reference: Create a query in SQL editor

NEW QUESTION # 75
Which of the following benefits of using Databricks SQL is provided by Data Explorer?

A. It can be used to produce dashboards that allow data exploration.
B. It can be used to make visualizations that can be shared with stakeholders.
C. It can be used to connect to third party Bl cools.
D. It can be used to view metadata and data, as well as view/change permissions.
E. It can be used to run UPDATE queries to update any tables in a database.

Answer: D

Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. Reference: Discover and manage data using Data Explorer

NEW QUESTION # 76
Which of the following statements about a refresh schedule is incorrect?

A. You must have workspace administrator privileges to configure a refresh schedule
B. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
C. Refresh schedules can be configured in the Query Editor.
D. A refresh schedule is not the same as an alert.
E. A query can be refreshed anywhere from 1 minute lo 2 weeks

Answer: B

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 # 77
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
B. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
C. The view has been converted into a table.
D. The view has been dropped.
E. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.

Answer: B

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
* Views do not store data themselves but reference data from underlying tables.
* Logging out or being inactive does not delete or alter views.
* Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
* Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.
eur_customers, and it will return the same data (provided the underlying table hasn't changed).
Reference: Views - Databricks Documentation

NEW QUESTION # 78
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