Databricks Certified Data Analyst Associate Practice Test 25

Databricks Certified Data Analyst Associate certification is a highly sought-after credential in the field of data analytics. This certification is designed for individuals who have a strong foundation in data analysis and are looking to validate their skills and knowledge in using Databricks for data analysis.

One of the key features of the Databricks Certified Data Analyst Associate certification is the comprehensive practice exam that is included as part of the certification process. This practice exam is designed to simulate the actual exam experience and allows candidates to assess their readiness for the certification exam. The practice exam covers all the key topics and concepts that are tested in the certification exam, giving candidates the opportunity to familiarize themselves with the format and structure of the exam.

Databricks Certified Data Analyst Associate certification is recognized as a mark of excellence in the field of data analytics. Employers value this certification as it demonstrates that the individual has the skills and knowledge needed to effectively analyze data using Databricks. This certification is a valuable asset for professionals looking to advance their careers in data analytics and stand out in a competitive job market.

To earn the Databricks Certified Data Analyst Associate certification, candidates must pass a rigorous exam that tests their knowledge and skills in using Databricks for data analysis. The exam covers a wide range of topics, including data manipulation, data visualization, data exploration, and data modeling. Candidates must demonstrate their ability to use Databricks to analyze data and derive meaningful insights that can drive business decisions.

Databricks Certified Data Analyst Associate certification is designed to validate the skills and knowledge of data analysts who use Databricks for data analysis. This certification is ideal for individuals who work with large datasets and need to analyze data efficiently and effectively. By earning this certification, data analysts can demonstrate their expertise in using Databricks to analyze data and provide valuable insights to their organizations.

Databricks Certified Data Analyst Associate certification is a valuable credential for data analysts who use Databricks for data analysis. This certification is highly respected in the field of data analytics and is recognized by employers as a mark of excellence. By earning this certification, data analysts can validate their skills and knowledge in using Databricks for data analysis and enhance their career prospects.

Databricks Certified Data Analyst Associate Exam Summary:

• Exam Name : Databricks Certified Data Analyst Associate

• Exam voucher cost: USD 200, plus applicable taxes as required per local law

• Delivery method: Online Proctored

• Test aides: none allowed.

• Length of exam: 90 minutes

• Prerequisite: None required; course attendance and six months of hands-on experience in

  Databricks is highly recommended

• Validity: 2 years

• Recertification: Recertification is required to maintain your certification status. To recertify,

• you must take the full exam.

• Unscored Content: Exams may include unscored items to gather statistical information for

  future use. These items are not identified on the form and do not impact your score, and

  additional time is factored into account for this content

Databricks Certified Data Analyst Associate Exam Syllabus Topics:

• Databricks SQL

• Data Management

• SQL in the Lakehouse

• Data Visualization and Dashboarding

• Analytics applications

Databricks SQL

• Describe the key audience and side audiences for Databricks SQL.

• Describe that a variety of users can view and run Databricks SQL dashboards as stakeholders.

• Describe the benefits of using Databricks SQL for in-Lakehouse platform data processing.

• Describe how to complete a basic Databricks SQL query.

• Identify Databricks SQL queries as a place to write and run SQL code.

• Identify the information displayed in the schema browser from the Query Editor page.

• Identify Databricks SQL dashboards as a place to display the results of multiple queries at once.

• Describe how to complete a basic Databricks SQL dashboard.

• Describe how dashboards can be configured to automatically refresh.

• Describe the purpose of Databricks SQL endpoints/warehouses.

• Identify Serverless Databricks SQL endpoint/warehouses as a quick-starting option.

• Describe the trade-off between cluster size and cost for Databricks SQL endpoints/warehouses.

• Identify Partner Connect as a tool for implementing simple integrations with a number of other data products.

• Describe how to connect Databricks SQL to ingestion tools like Fivetran.

• Identify the need to be set up with a partner to use it for Partner Connect.

• Identify small-file upload as a solution for importing small text files like lookup tables and quick data integrations.

• Import from object storage using Databricks SQL.

• Identify that Databricks SQL can ingest directories of files of the files are the same type.

• Describe how to connect Databricks SQL to visualization tools like Tableau, Power BI, and Looker.

• Identify Databricks SQL as a complementary tool for BI partner tool workflows.

• Describe the medallion architecture as a sequential data organization and pipeline system of progressively cleaner data.

• Identify the gold layer as the most common layer for data analysts using Databricks SQL.

• Describe the cautions and benefits of working with streaming data.

• Identify that the Lakehouse allows the mixing of batch and streaming workloads.

  
  

Data Management

• Describe Delta Lake as a tool for managing data files.

• Describe that Delta Lake manages table metadata.

• Identify that Delta Lake tables maintain history for a period of time.

• Describe the benefits of Delta Lake within the Lakehouse.

• Describe persistence and scope of tables on Databricks.

• Compare and contrast the behavior of managed and unmanaged tables.

• Identify whether a table is managed or unmanaged.

• Explain how the LOCATION keyword changes the default location of database contents.

• Use Databricks to create, use, and drop databases, tables, and views.

• Describe the persistence of data in a view and a temp view

• Compare and contrast views and temp views.

• Explore, preview, and secure data using Data Explorer.

• Use Databricks to create, drop, and rename tables.

• Identify the table owner using Data Explorer.

• Change access rights to a table using Data Explorer.

• Describe the responsibilities of a table owner.

• Identify organization-specific considerations of PII data

  
  

SQL in the Lakehouse

• Identify a query that retrieves data from the database with specific conditions

• Identify the output of a SELECT query

• Compare and contrast MERGE INTO, INSERT TABLE, and COPY INTO.

• Simplify queries using subqueries.

• Compare and contrast different types of JOINs.

• Aggregate data to achieve a desired output.

• Manage nested data formats and sources within tables.

• Use cube and roll-up to aggregate a data table.

• Compare and contrast roll-up and cube.

• Use windowing to aggregate time data.

• Identify a benefit of having ANSI SQL as the standard in the Lakehouse.

• Identify, access, and clean silver-level data.

• Utilize query history and caching to reduce development time and query latency.

• Optimize performance using higher-order Spark SQL functions.

• Create and apply UDFs in common scaling scenarios.

  
  

Data Visualization and Dashboarding

• Create basic, schema-specific visualizations using Databricks SQL.

• Identify which types of visualizations can be developed in Databricks SQL (table, details, counter, pivot).

• Explain how visualization formatting changes the reception of a visualization

• Describe how to add visual appeal through formatting

• Identify that customizable tables can be used as visualizations within Databricks SQL.

• Describe how different visualizations tell different stories.

• Create customized data visualizations to aid in data storytelling.

• Create a dashboard using multiple existing visualizations from Databricks SQL Queries.

• Describe how to change the colors of all of the visualizations in a dashboard.

• Describe how query parameters change the output of underlying queries within a dashboard

• Identify the behavior of a dashboard parameter

• Identify the use of the "Query Based Dropdown List" as a way to create a query parameter from the distinct output of a different query.

• Identify the method for sharing a dashboard with up-to-date results.

• Describe the pros and cons of sharing dashboards in different ways

• Identify that users without permission to all queries, databases, and endpoints can easily refresh a dashboard using the owner's credentials.

• Describe how to configure a refresh schedule

• Identify what happens if a refresh rate is less than the Warehouse's "Auto Stop"

• Describe how to configure and troubleshoot a basic alert

• Describe how notifications are sent when alerts are set up based on the configuration

  
  

Analytics applications

• Compare and contrast discrete and continuous statistics.

• Describe descriptive statistics.

• Describe key moments of statistical distributions.

• Compare and contrast key statistical measures.

• Describe data enhancement as a common analytics application.

• Enhance data in a common analytics application.

• Identify a scenario in which data enhancement would be beneficial.

• Describe the blending of data between two source applications.

• Identify a scenario in which data blending would be beneficial.

• Perform last-mile ETL as project-specific data enhancement

  
  

In conclusion, the Databricks Certified Data Analyst Associate certification is a valuable credential for data analysts who use Databricks for data analysis. This certification is highly respected in the field of data analytics and is recognized by employers as a mark of excellence. By earning this certification, data analysts can validate their skills and knowledge in using Databricks for data analysis and enhance their career prospects.

DISCLAIMER : These questions are designed to, give you a feel of the level of questions asked in the actual exam. We are not affiliated with Databricks or Apache. All the screenshots added to the answer explanation are not owned by us. Those are added just for reference to the context.