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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which example of data projects represents a common analytics application to be completed in Databricks SQL?

A. Monitoring the performance of deployed machine learning solutions
B. Performing analysis-specific ETL on gold-layer tables
C. Testing the latency of real-time streaming workloads
D. Predicting customer churn by automatically developing time-series models

Answer: B

Explanation:
Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a "last-mile" analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.

NEW QUESTION # 23
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. ACID transactions
B. Flexible schemas
C. Open-source formats
D. Scalable storage
E. Data deletion

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 # 24
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 an exact substitute with the same level of functionality
B. As a substitute with less functionality
C. As a complementary tool for quick in-platform Bl work
D. As a complementary tool for professional-grade presentations
E. As a complete replacement with additional functionality

Answer: C

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. References: 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 # 25
A data analyst wants the following output:
customer_name number_of_orders
John Doe 388
Zhang San 234
Which statement will produce this output?

A. SELECT customerjiame, count(order_id)
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id GROUP BY customerjiame;
B. SELECT customer_name, count(order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id USE customer_name;
C. SELECT customerjiame, (order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id;
D. SELECT customer_name, count(order_id) AS number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id
GROUP BY customer_name;

Answer: D

NEW QUESTION # 26
Which location can be used to determine the owner of a managed table?

A. Review the Owner field in the table page using the SQL Editor
B. Review the Owner field in the schema page using Data Explorer
C. Review the Owner field in the database page using Data Explorer
D. Review the Owner field in the table page using Catalog Explorer

Answer: D

Explanation:
In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:
Access Catalog Explorer:
In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.
Navigate to the Table:
Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.
View Table Details:
Click on the table name to open its details page.
Identify the Owner:
On the table's details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.
This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.

NEW QUESTION # 27
......

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