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

Thema	Einzelheiten
Thema 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.
Thema 2	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 3	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 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.
Thema 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.

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q90-Q95):

90. Frage
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Cluster Event Log
B. Executor's log file
C. Ganglia
D. Workspace audit logs
E. Driver's log file

Antwort: A

Begründung:
Option D is correct. Cluster resizing caused by autoscaling is recorded as a cluster activity event, so the administrator should review the Cluster Event Log. Driver and executor logs are useful for application
/runtime troubleshooting, not for the cluster resizing timeline. Ganglia shows metrics but is not the event timeline. Official Databricks documentation identifies cluster activity events as the place to retrieve cluster activity, and Databricks autoscaling documentation describes resize events such as "Cluster resize request started."

91. Frage
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

A. Automated alerting systems
B. Simultaneous multi-user support
C. Computations, data, and analytical tools on the same platform
D. Advanced dashboarding capabilities

Antwort: C

Begründung:
Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.
Reference: Databricks AI & BI: Transform Data into Actionable Insights

92. Frage
In which circumstance will there be a substantial difference between the variable's mean and median values?

A. When the variable is of the categorical type
B. When the variable is of the boolean type
C. When the variable contains a lot of extreme outliers
D. When the variable contains no outliers

Antwort: C

Begründung:
The mean is sensitive to extreme values, often called outliers, which can significantly skew the average away from the true center of the data. The median, however, is a measure of central tendency that is resistant to such outliers because it only considers the middle value(s) when the data is ordered. Therefore, when a variable contains many extreme outliers, there will be a substantial difference between the mean and the median. According to Databricks data analysis materials, this is a fundamental concept when choosing summary statistics for reporting.

93. Frage
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
B. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
C. Running SELECT * FROM accounts.customers will return all rows in the table.
D. All files with the .customers extension are deleted.
E. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.

Antwort: A

Begründung:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

94. Frage
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. SQL analyst
B. Business intelligence analyst
C. Business analyst
D. Data analyst
E. Data engineer

Antwort: E

Begründung:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. References: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

95. Frage
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