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Databricks-Certified-Professional-Data-Engineer Musterprüfungsfragen - Databricks-Certified-Professional-Data-EngineerZertifizierung & Databricks-Certified-Professional-Data-EngineerTestfagen

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q32-Q37):

32. Frage
Drop the customers database and associated tables and data, all of the tables inside the database are managed tables. Which of the following SQL commands will help you accomplish this?

A. DROP DATABASE customers FORCE
B. DROP DELTA DATABSE customers
C. DROP DATABASE customers CASCADE
D. DROP DATABASE customers INCLUDE
E. All the tables must be dropped first before dropping database

Antwort: D

Begründung:
Explanation
The answer is DROP DATABASE customers CASCADE
Drop database with cascade option drops all the tables, since all of the tables inside the database are managed tables we do not need to perform any additional steps to clean the data in the storage.

33. Frage
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table namedusers.

Assuming thatuser_idis a unique identifying key and thatdelete_requestscontains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
C. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
D. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Antwort: E

Begründung:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

34. Frage
The data analyst team had put together queries that identify items that are out of stock based on orders and replenishment but when they run all together for final output the team noticed it takes a really long time, you were asked to look at the reason why queries are running slow and identify steps to improve the performance and when you looked at it you noticed all the code queries are running sequentially and using a SQL endpoint cluster. Which of the following steps can be taken to resolve the issue?
Here is the example query
1.--- Get order summary
2.create or replace table orders_summary
3.as
4.select product_id, sum(order_count) order_count
5.from
6. (
7. select product_id,order_count from orders_instore
8. union all
9. select product_id,order_count from orders_online
10. )
11.group by product_id
12.-- get supply summary
13.create or repalce tabe supply_summary
14.as
15.select product_id, sum(supply_count) supply_count
16.from supply
17.group by product_id
18.
19.-- get on hand based on orders summary and supply summary
20.
21.with stock_cte
22.as (
23.select nvl(s.product_id,o.product_id) as product_id,
24. nvl(supply_count,0) - nvl(order_count,0) as on_hand
25.from supply_summary s
26.full outer join orders_summary o
27. on s.product_id = o.product_id
28.)
29.select *
30.from
31.stock_cte
32.where on_hand = 0

A. Turn on the Auto Stop feature for the SQL endpoint.
B. Increase the maximum bound of the SQL endpoint's scaling range.
C. Turn on the Serverless feature for the SQL endpoint and change the Spot Instance Pol-icy to "Reliability Optimized."
D. Turn on the Serverless feature for the SQL endpoint.
E. Increase the cluster size of the SQL endpoint.

Antwort: E

Begründung:
Explanation
The answer is to increase the cluster size of the SQL Endpoint, here queries are running sequentially and since the single query can not span more than one cluster adding more clusters won't improve the query but rather increasing the cluster size will improve performance so it can use additional compute in a warehouse.
In the exam please note that additional context will not be given instead you have to look for cue words or need to understand if the queries are running sequentially or concurrently. if the que-ries are running sequentially then scale up(more nodes) if the queries are running concurrently (more users) then scale out(more clusters).
Below is the snippet from Azure, as you can see by increasing the cluster size you are able to add more worker nodes.

SQL endpoint scales horizontally(scale-out) and vertically (scale-up), you have to understand when to use what.
Scale-up-> Increase the size of the cluster from x-small to small, to medium, X Large....
If you are trying to improve the performance of a single query having additional memory, additional nodes and cpu in the cluster will improve the performance.
Scale-out -> Add more clusters, change max number of clusters
If you are trying to improve the throughput, being able to run as many queries as possible then having an additional cluster(s) will improve the performance.
SQL endpoint
A picture containing diagram Description automatically generated

35. Frage
What is the top-level object in unity catalog?

A. Workspace
B. Catalog
C. Database
D. Metastore
E. Table

Antwort: D

Begründung:
Explanation
Key concepts - Azure Databricks | Microsoft Docs

Diagram Description automatically generated

36. Frage
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the pipelines, reset, allowed property to false on bpm_stats
B. Set the pipelines, reset, allowed property to false on raw_iot
C. Set the skipChangeCommits flag to true on bpm_stats
D. Set the SkipChangeCommits flag to true raw_lot

Antwort: B

Begründung:
In Databricks Lakehouse, to retain manually deleted or updated records in theraw_iottable while recomputing downstream tables when a pipeline update is run, the propertypipelines.reset.allowedshould be set tofalse.
This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to theraw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
References:
* Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live- tables/delta-live-tables-overview.html

37. Frage
......

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