Databricks-Certified-Data-Engineer-Associate Fragen Und Antworten - Databricks-Certified-Data-Engineer-Associate Lernhilfe

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	DE-A
Exam Duration:	90 minutes
Real Exam Qty:	45 scored multiple-choice questions
Passing Score:	Approximately ~70% (not officially fixed/publicly guaranteed)
Exam Price:	USD 200
Available Languages:	English
Exam Format:	Multiple-choice, Scenario-based questions, Proctored online or test center
Related Certifications:	Databricks Certified Data Engineer Professional
Certificate Validity Period:	2 years
Recommended Training:	Databricks Data Engineering Learning Path (Databricks Academy) Official Data Engineering with Databricks Course
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam or test center
Pre Condition:	No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

>> Databricks-Certified-Data-Engineer-Associate Fragen Und Antworten <<

Databricks-Certified-Data-Engineer-Associate Lernhilfe - Databricks-Certified-Data-Engineer-Associate Zertifikatsfragen

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q28-Q33):

28. Frage
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
B. They can set the query's refresh schedule to end after a certain number of refreshes.
C. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
D. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
E. They can set the query's refresh schedule to end on a certain date in the query scheduler.

Antwort: E

Begründung:
Explanation
If a dashboard is configured for automatic updates, it has a Scheduled button at the top, rather than a Schedule button. To stop automatically updating the dashboard and remove its subscriptions:
Click Scheduled.
In the Refresh every drop-down, select Never.
Click Save. The Scheduled button label changes to Schedule.
Source:https://learn.microsoft.com/en-us/azure/databricks/sql/user/dashboards/

29. Frage
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The names of the files to be copied were not included with the FILES keyword.
B. The PARQUET file format does not support COPY INTO.
C. The previous day's file has already been copied into the table.
D. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
E. The COPY INTO statement requires the table to be refreshed to view the copied rows.

Antwort: C

Begründung:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. Reference: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

30. Frage
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. org.apache.spark.sql.sqlite
B. DELTA
C. autoloader
D. org.apache.spark.sql.jdbc
E. sqlite

Antwort: E

Begründung:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

31. Frage
Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

A. region_sales = sales_df.groupBy("region").agg(sum("sales_amountM).alias("total_sales_amount"))
B. region sales - sales_df.agg(sum("sales_amount").groupBy("region").alias("total sales amount"))
C. region_sales= sales_df.groupBy("category").sum(nsales_amount").alias("t_otal_sales_amounl")
D. region_sales = sales_df. sum ("salen_aiTiount") . groupBy ("region") .alias ("total_sale3_amount")

Antwort: A

32. Frage
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

A. SUM
B. TRANSFORM
C. CONVERT
D. PIVOT
E. WHERE

Antwort: D

Begründung:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
Reference:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]

33. Frage
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