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

Thema	Einzelheiten
Thema 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.
Thema 2	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 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.
Thema 4	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 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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Databricks-Certified-Data-Analyst-Associate ist eine der Databricks Zertifizierungsprüfungen. IT-Fachmann mit Databricks Zertifikat sind sehr beliebt in der IT-Branche. Deshalb legen imme mehr Leute die Databricks-Certified-Data-Analyst-Associate Zertifizierungsprüfung. Jedoch ist es nicht so einfach, die Databricks Databricks-Certified-Data-Analyst-Associate Zertifizierungsprüfung zu bestehen. Wenn Sie nicht an den entprechenden Kursen teilnehmen, brauchen Sie viel Zeit und Energie, sich auf die Prüfung vorzubereiten. Nun kann ITZert Ihnen viel Zeit und Energie ersparen.

Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q19-Q24):

19. 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: E

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. Reference: The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

20. Frage
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?

Antwort: D

Begründung:
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.

21. Frage
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. Ad-hoc improvements
B. Data testing
C. Last-mile ETL
D. Last-mile
E. Data enhancement

Antwort: E

Begründung:
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

22. Frage
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. Gold
B. All of these layers are used equally by data analysts
C. Bronze
D. None of these layers are used by data analysts
E. Silver

Antwort: A

Begründung:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. Reference: What is the medallion lakehouse architecture?

23. Frage
The stakeholders.customers table has 15 columns and 3,000 rows of dat
a. 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 has been converted into a table.
B. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
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 has been dropped.
E. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.

Antwort: E

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).

24. Frage
......

Mit der Entwicklung der IT-Industrie nimmt die Zahl der IT-Lerner seit Jahren immer zu. Das führt zu immer stärkerer Konkurrenzen. Und es ist undenkbar, dass Sie in IT-Industrie von anderen überschritten sind. Deshalb sollen Sie Ihre Fähigkeit ständig erhöhen und Ihre Stärke zu anderen beweisen. Wie können Sie Ihre Fähigkeit zu anderen beweisen? Immer mehr Leute wählen IT-Zertifizierungen, Ihre Fähigkeit zu beweisen. Wollen Sie auch? Kommen Sie zuerst zu Databricks Databricks-Certified-Data-Analyst-Associate Zertifizierungsprüfung. Das ist die wichtigste Databricks Prüfung und auch von vielen Unternehmen anerkannt.

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Databricks-Certified-Data-Analyst-Associate Schulungsangebot, Databricks-Certified-Data-Analyst-Associate Testing Engine, Databricks Certified Data Analyst Associate Exam Trainingsunterlagen

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q19-Q24):

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