Databricks-Certified-Data-Analyst-Associate Exam & Databricks-Certified-Data-Analyst-Associate Ausbildungsressourcen

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Databricks Databricks-Certified-Data-Analyst-Associate Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 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.
Thema 3	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.
Thema 4	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.
Thema 5	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.

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q60-Q65):

60. Frage
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

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

Antwort: B

Begründung:
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. References: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

61. Frage
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age > = 75 AND country = ' canada ' ;
Which of the following tables represents the output of the above command?

Antwort: A

Begründung:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

62. Frage
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 FROM customer_summary
C. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
D. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
E. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

Antwort: C

Begründung:
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

63. Frage
A Data Analyst is working on sensor_df; this DataFrame contains two columns: record_datetime timestamp and record array.
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?

A. exploded_df = sensor_df.withColumn( " record_exploded " , explode( " record " ))exploded_df = exploded_df.select( " record_datetime " , " record_exploded.sensor_id " , " record_exploded.status " , " record_exploded.health " )
B. exploded_df = exploded_df.select( " record_datetime " , " record_exploded " )
C. Uses withColumn, but selects sensor_id, status, and health as if they are already top-level columns.
D. Selects nested fields before correctly creating the exploded column.

Antwort: A

Begründung:
Option C is correct after correcting the formatting and typing errors in the uploaded option text. The analyst needs explode( " record " ) because the record column is an array, and the requirement is to return one array item per row. Then the analyst must select fields from the exploded struct using dot notation, such as record_exploded.sensor_id, record_exploded.status, and record_exploded.health. Databricks PySpark documentation states that explode "returns a new row for each element in the given array or map," and withColumn returns a new DataFrame by adding or replacing a column. The select method projects expressions or column names into the resulting DataFrame.

64. Frage
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
B. The view has been dropped.
C. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
D. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
E. The view has been converted into a table.

Antwort: D

Begründung:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
* Views do not store data themselves but reference data from underlying tables.
* Logging out or being inactive does not delete or alter views.
* Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
* Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.
eur_customers, and it will return the same data (provided the underlying table hasn't changed).
Reference: Views - Databricks Documentation

65. Frage
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