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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to add two separate visualizations to the dashboard based on the same Query.
B. They will need to decide on a single data visualization to add to the dashboard.
C. They will need to copy the Query and create one data visualization per query.
D. They will need to create two separate dashboards.
E. They will need to alter the Query to return two separate sets of results.

Answer: A

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 101
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be applied at scale to array data objects
B. When custom logic needs to be applied to simple, unnested data
C. When built-in functions need to run through the Catalyst Optimizer
D. When custom logic needs to be converted to Python-native code
E. When built-in functions are taking too long to perform tasks

Answer: A

Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher- order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. References: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

NEW QUESTION # 102
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

A. Last-mile
B. Data blending
C. Data testing
D. Last-mile ETL
E. Data enhancement

Answer: D

Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL

NEW QUESTION # 103
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Data Explorer
B. Databricks Filesystem
C. Dashboards
D. Jobs
E. Repos

Answer: A

Explanation:
Option E is correct. In the current Databricks UI, Data Explorer is known as Catalog Explorer. It is the location used to browse catalogs, schemas, tables, and views, review object metadata, and request or inspect access-related information. DBFS, Jobs, Dashboards, and Repos are not the table-permission review location.
Official Databricks extract: users can navigate the catalog tree, open an object's page, and request additional privileges from Catalog Explorer.

NEW QUESTION # 104
What describes Partner Connect in Databricks?

A. it allows for free use of Databricks partner tools through a common API.
B. It is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly known as a SQL endpoint).
C. it allows multi-directional connection between Databricks and Databricks partners easier.
D. It exposes connection information to third-party tools via Databricks partners.

Answer: C

Explanation:
Databricks Partner Connect is designed to simplify and streamline the integration between Databricks and its technology partners. It provides a unified interface within the Databricks platform that facilitates the discovery and connection to a variety of data, analytics, and AI tools. By automating the configuration of necessary resources such as clusters, tokens, and connection files, Partner Connect enables seamless, bi- directional data flow between Databricks and partner solutions. This integration enhances the overall functionality of the Databricks Lakehouse by allowing users to easily incorporate external tools and services into their workflows, thereby expanding the platform ' s capabilities and fostering a more cohesive data ecosystem.
Reference: Discover Databricks Partner Connect

NEW QUESTION # 105
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