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Databricks-Certified-Data-Engineer-Professional Torrent Anleitung - Databricks-Certified-Data-Engineer-Professional Studienführer & Databricks-Certified-Data-Engineer-Professional wirkliche Prüfung

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q20-Q25):

20. Frage
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

A. A user can only transfer job ownership to a group if they are also a member of that group.
B. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C. Only workspace administrators can grant "Owner" privileges to a group.
D. Other than the default "admins" group, only individual users can be granted privileges on jobs.
E. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

Antwort: B

Begründung:
A job cannot have more than one owner. A job cannot have a group as an owner.

21. Frage
Which statement describes Delta Lake optimized writes?

A. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
B. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.

Antwort: D

Begründung:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

22. Frage
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Delta log is scanned for min and max statistics for the latitude column
B. All records are cached to an operational database and then the filter is applied
C. The Parquet file footers are scanned for min and max statistics for the latitude column
D. The Hive metastore is scanned for min and max statistics for the latitude column
E. All records are cached to attached storage and then the filter is applied Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Antwort: A

Begründung:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.

23. Frage
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
D. Because User A created the jobs, their identity will be associated with both the job creation Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from events and the job run events.
E. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Antwort: B

Begründung:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

24. Frage
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
C. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Antwort: D

Begründung:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

25. Frage
......

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Elijah Baker Elijah Baker

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