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Databricks-Certified-Data-Analyst-Associate Exam Dumps

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Description

Exam Name: Databricks Certified Data Analyst Associate Exam
Exam Code: Databricks-Certified-Data-Analyst-Associate
Related Certification(s): Databricks Certified Data Analyst Certification
Certification Provider: Databricks
Actual Exam Duration: 90 Minutes
Number of Databricks-Certified-Data-Analyst-Associate practice questions in our database: 
Expected Databricks-Certified-Data-Analyst-Associate Exam Topics, as suggested by Databricks :

  • Module 1: Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints/warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint/warehouses, trade-off between cluster size and cost for Databricks SQL endpoints/warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
  • Module 2: Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
  • Module 3: SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrast MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
  • Module 4: Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
  • Module 5: Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

Description

Exam Name: Databricks Certified Data Analyst Associate Exam
Exam Code: Databricks-Certified-Data-Analyst-Associate
Related Certification(s): Databricks Certified Data Analyst Certification
Certification Provider: Databricks
Actual Exam Duration: 90 Minutes
Number of Databricks-Certified-Data-Analyst-Associate practice questions in our database: 
Expected Databricks-Certified-Data-Analyst-Associate Exam Topics, as suggested by Databricks :

  • Module 1: Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints/warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint/warehouses, trade-off between cluster size and cost for Databricks SQL endpoints/warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
  • Module 2: Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
  • Module 3: SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrast MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
  • Module 4: Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
  • Module 5: Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

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Q1. A data analyst has created a user-defined function using the following line of code: CREATE FUNCTION price(spend DOUBLE, units DOUBLE) RETURNS DOUBLE RETURN spend / units; Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

A.SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary

B. SELECT price FROM customer_summary

C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

D. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

E. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

Correct Answer: E

Q2. 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.Data page

B. Dashboards page

C. Queries page

D. Alerts page

E. SQL Editor page

Correct Answer: E

Q3. A data analyst has created a user-defined function using the following line of code: CREATE FUNCTION price(spend DOUBLE, units DOUBLE) RETURNS DOUBLE RETURN spend / units; Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

A.SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary

B. SELECT price FROM customer_summary

C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

D. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

E. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

Correct Answer: E

Q4. 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.Data page

B. Dashboards page

C. Queries page

D. Alerts page

E. SQL Editor page

Correct Answer: E

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