최신업데이트된Databricks-Certified-Professional-Data-Engineer최신덤프자료덤프문제

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Databricks Certified Professional Data Engineer Certification 시험은 Databricks 플랫폼에서 복잡한 데이터 솔루션을 구축하고 관리 할 때 데이터 전문가의 기술과 전문 지식을 검증하는 권위있는 인증입니다. 이 인증은 협업 환경에서 데이터 엔지니어링 워크 플로를 설계, 구현 및 관리하는 데 필요한 지식과 기술을 테스트하도록 설계되었습니다. 이 시험은 Databricks Technologies를 사용하여 확장 가능하고 안정적인 데이터 솔루션을 설계하고 구현하는 기능을 검증합니다.

Databricks Certified Professional Data Engineer 자격증 시험은 60개의 객관식 문제로 구성되어 있으며, 90분 내에 답변해야합니다. 이 문제는 후보자의 Databricks 및 해당 기능에 대한 지식뿐만 아니라 이러한 지식을 실제 상황에 적용할 수 있는 능력을 시험합니다. 시험에 합격하려면 후보자는 최소 70%의 합격 점수를 얻어야 합니다. 이 자격증은 2년간 유효하며, 시험을 다시 보거나 지속 교육 활동 집필을 완료하여 갱신할 수 있습니다. Databricks Certified Professional Data Engineer 자격증을 취득하면 데이터 전문가는 Databricks를 사용하여 데이터 엔지니어링 솔루션을 구축하고 배포하는 전문 기술을 증명할 수 있으며, 이는 오늘날 데이터 중심 비즈니스 환경에서 가치 있는 자산이 될 수 있습니다.

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최근 더욱 많은 분들이Databricks인증Databricks-Certified-Professional-Data-Engineer시험에 도전해보려고 합니다. ITDumpsKR에서는 여러분들의 시간돠 돈을 절약해드리기 위하여 저렴한 가격에 최고의 품질을 지닌 퍼펙트한Databricks인증Databricks-Certified-Professional-Data-Engineer시험덤플르 제공해드려 고객님의 시험준비에 편안함을 선물해드립니다. ITDumpsKR제품을 한번 믿어보세요.

이 시험은 Databricks를 사용하여 데이터 솔루션을 디자인하고 구현하는 경험을 갖고 있는 데이터 엔지니어를 대상으로 합니다. 이 인증을 위한 후보자는 데이터 엔지니어링 개념, 데이터 처리 프레임워크 및 Python, SQL과 같은 프로그래밍 언어에 대한 좋은 이해도를 가져야 합니다. 또한 AWS, Azure 및 Google Cloud Platform과 같은 클라우드 플랫폼에 익숙해야 합니다.

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q53-Q58):

질문 # 53
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.
address THEN
UPDATE SET current = false, end_date = staged_updates.effective_date
WHEN NOT MATCHED THEN
INSERT (customer_id, address, current, effective_date, end_date)
VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
* The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

A. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

정답：B

설명：
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

질문 # 54
The below spark command is looking to create a summary table based customerId and the number of times the customerId is present in the event_log delta table and write a one-time micro-batch to a summary table, fill in the blanks to complete the query.
1.spark._________
2. .format("delta")
3. .table("events_log")
4. .groupBy("customerId")
5. .count()
6. ._______
7. .format("delta")
8. .outputMode("complete")
9. .option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/")
10. .trigger(______)
11. .table("target_table")

A. readStream, writeStream, once = True
B. writeStream, readStream, once
C. writeStream, processingTime = once
D. readStream, writeStream, once
E. writeStream, readStream, once = True

정답：A

설명：
Explanation
The answer is readStream, writeStream, once = True.
spark.readStream
format("delta")
table("events_log")
groupBy("customerId")
count()
writeStream
format("delta")
outputMode("complete")
option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/") trigger(once = True) table("target_table")

질문 # 55
Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

A. Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard
B. A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.
C. Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.
D. Use Incremental refresh to run at 8 AM every day.
E. The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.

정답：E

설명：
Explanation
The answer is,
The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at
8 AM.
Automatically refresh a dashboard
A dashboard's owner and users with the Can Edit permission can configure a dashboard to auto-matically refresh on a schedule. To automatically refresh a dashboard:
* Click the Schedule button at the top right of the dashboard. The scheduling dialog appears.
* Graphical user interface, text, application, email, Teams Description automatically generated
* 2.In the Refresh every drop-down, select a period.
* 3.In the SQL Warehouse drop-down, optionally select a SQL warehouse to use for all the queries.
If you don't select a warehouse, the queries execute on the last used SQL ware-house.
* 4.Next to Subscribers, optionally enter a list of email addresses to notify when the dashboard is automatically updated.
* Each email address you enter must be associated with a Azure Databricks account or con-figured as an alert destination.
* 5.Click Save. The Schedule button label changes to Scheduled.

질문 # 56
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data . They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline.
Which configuration should the team use when writing data to the Delta table?

A. validateSchema = false
B. mergeSchema = true
C. ignoreChanges = false
D. overwriteSchema = true

정답：B

질문 # 57
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited

정답：C

설명：
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active.
Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html

질문 # 58
......

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