VISUAL DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE CERT EXAM, DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE RELIABLE TEST PREP

Visual Databricks-Certified-Data-Analyst-Associate Cert Exam, Databricks-Certified-Data-Analyst-Associate Reliable Test Prep

Visual Databricks-Certified-Data-Analyst-Associate Cert Exam, Databricks-Certified-Data-Analyst-Associate Reliable Test Prep

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

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • 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 contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

  • A. DROP TABLE table_name FROM database_name;
  • B. DROP TABLE database_name.table_name;
  • C. DELETE TABLE database_name.table_name;
  • D. DELETE TABLE table_name FROM database_name;
  • E. DROP DATABASE database_name;

Answer: B


NEW QUESTION # 13
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 double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
  • B. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
  • C. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
  • D. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
  • E. SELECT price FROM customer_summary

Answer: B

Explanation:
A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary. Reference:
What are user-defined functions (UDFs)?
User-defined scalar functions - SQL
V


NEW QUESTION # 14
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

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

Answer: E

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 # 15
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 complementary tool for quick in-platform Bl work
  • C. As a substitute with less functionality
  • D. As a complete replacement with additional functionality
  • E. As a complementary tool for professional-grade presentations

Answer: B

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. Reference: 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 # 16
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?

  • A. Tableau
  • B. Databricks Machine Learning
  • C. Delta Lake
  • D. Databricks Notebooks
  • E. Databricks SQL

Answer: E

Explanation:
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. Reference:
Databricks SQL
Query data using SQL Analytics
Visualizations in Databricks notebooks
Delta Lake


NEW QUESTION # 17
......

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