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

Thema	Einzelheiten
Thema 1	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 2	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 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	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 5	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.

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q57-Q62):

57. Frage
A data analyst needs to share a Databricks SQL dashboard with stakeholders that are not permitted to have accounts in the Databricks deployment. The stakeholders need to be notified every time the dashboard is refreshed.
Which approach can the data analyst use to accomplish this task with minimal effort/

A. By granting the stakeholders' email addresses to the SQL Warehouse (formerly known as endpoint) subscribers list
B. By downloading the dashboard as a PDF and emailing it to the stakeholders each time it is refreshed
C. By adding the stakeholders' email addresses to the refresh schedule subscribers list
D. By granting the stakeholders' email addresses permissions to the dashboard

Antwort: C

Begründung:
To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders' email addresses to the dashboard's refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.

58. Frage
Where can an admin or data owner grant database, table, and view permissions to a group?

A. Settings
B. Dashboard
C. SQL Warehouses
D. Data

Antwort: D

Begründung:
Option B is correct. In the older Databricks SQL UI terminology used by this question, permissions for databases, tables, and views are managed from the Data area. In the current Databricks UI, this is handled through Catalog Explorer, where an admin or data owner opens the object, goes to the Permissions tab, clicks Grant, selects users or groups, and grants the required privileges. Dashboards are for reporting, SQL Warehouses provide compute, and Settings is not the object-level permission management area. Official Databricks documentation says privileges can be managed using SQL commands, CLI, Terraform, or Catalog Explorer, and the UI flow is: open the table details page, go to Permissions, click Grant, select users or groups, and assign privileges.

59. Frage
How can a data analyst determine if query results were pulled from the cache?

A. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
B. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
C. Go to the Alerts tab and check the Cache Status alert.
D. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
E. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.

Antwort: E

Begründung:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status.
Reference link: Databricks SQL - Query Cache

60. Frage
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. Databricks SQL
B. Delta Lake
C. Databricks Notebooks
D. Tableau
E. Databricks Machine Learning

Antwort: A

Begründung:
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

61. Frage
Which of the following data workloads will utilize a Gold table as its source?

A. A job that ingests raw data from a streaming source into the Lakehouse.
B. A job that queries aggregated data designed to feed into a dashboard.
C. A job that enriches data by parsing its timestamps into a human-readable format.
D. A job that cleans data by removing malformatted records.
E. A job that aggregates uncleaned data to create standard summary statistics.

Antwort: B

Begründung:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

62. Frage
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