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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A business analyst has been asked to create a data entity/object called sales_by_employee. It should always stay up-to-date when new data are added to the sales table. The new entity should have the columns sales_person, which will be the name of the employee from the employees table, and sales, which will be all sales for that particular sales person. Both the sales table and the employees table have an employee_id column that is used to identify the sales person.
Which of the following code blocks will accomplish this task?

Answer: B

Explanation:
The SQL code provided in Option D is the correct way to create a view named sales_by_employee that will always stay up-to-date with the sales and employees tables. The code uses the CREATE OR REPLACE VIEW statement to define a new view that joins the sales and employees tables on the employee_id column. It selects the employee_name as sales_person and all sales for each employee, ensuring that the data entity
/object is always up-to-date when new data are added to these tables.
The answer can be verified from Databricks SQL documentation which provides insights on creating views using SQL queries, joining tables, and selecting specific columns to be included in the view. Reference link: Databricks SQL

NEW QUESTION # 57
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Workflows to establish a cluster for Fivetran to interact with
B. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
C. Use Delta Live Tables to establish a cluster for Fivetran to interact with
D. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
E. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

Answer: D

Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

NEW QUESTION # 58
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
B. They can turn on the Auto Stop feature for the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range.
D. They can increase the cluster size of the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint.

Answer: C

Explanation:
Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: "You can increase the maximum clusters if you want to handle more concurrent users," and Databricks recommends monitoring queued queries and adjusting maximum clusters.

NEW QUESTION # 59
What describes the variance of a set of values?

A. Variance is a measure of how far a single observed value is from a set ot va IN
B. Variance is a measure of central tendency of a set of values.
C. Variance is a measure of how far an observed value is from the variable ' s maximum or minimum value.
D. Variance is a measure of how far a set of values is spread out from the sets central value.

Answer: D

Explanation:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1 Reference: Variance - Wikipedia

NEW QUESTION # 60
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. Data testing
B. Ad-hoc improvements
C. Last-mile
D. Last-mile ETL
E. Data enhancement

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 # 61
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