Databricks Databricks-Certified-Professional-Data-Engineer인증시험대비공부자료, Databricks-Certified-Professional-Data-Engineer최신버전덤프공부문제

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Databricks Certified Professional Data Engineer 시험은 후보자가 Databricks를 사용하여 일련의 작업을 완료해야 하는 실습 시험입니다. 이 시험은 후보자의 데이터 파이프라인 설계 및 구현 능력, 데이터 원본 및 시차 처리 작업, Databricks를 사용한 변환 수행 능력을 평가합니다. 또한 이 시험은 후보자가 성능 및 신뢰성을 위한 데이터 파이프라인을 최적화하고 조정하는 능력을 시험합니다.

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Databricks-Certified-Professional-Data-Engineer최신버전 덤프공부문제 & Databricks-Certified-Professional-Data-Engineer최신 업데이트 인증시험자료

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Databricks Certified Professional Data Engineer 시험은 데이터 수집, 변환, 스토리지 및 처리와 같은 다양한 주제를 다룹니다. 이 시험은 후보자의 데이터 사업 도구 및 기술을 사용하여 실제 문제와 도전을 해결하는 능력을 테스트합니다. 이 시험을 통과 한 응시자는 빅 데이터 처리 및 분석을위한 주요 클라우드 기반 플랫폼 인 Databricks를 사용하여 데이터 파이프 라인 설계, 구축 및 관리 능력을 보여줍니다.

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q75-Q80):

질문 # 75
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

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

정답：B

설명：
Explanation
This is the best configuration for scheduling Structured Streaming jobs for production, as it automatically recovers from query failures and keeps costs low. A new job cluster is created for each run of the job and terminated when the job completes, which saves costs and avoids resource contention. Retries are not needed for Structured Streaming jobs, as they can automatically recover from failures using checkpointing and write-ahead logs. Maximum concurrent runs should be set to 1 to avoid duplicate output or data loss. Verified References: Databricks Certified Data Engineer Professional, under "Monitoring & Logging" section; Databricks Documentation, under "Schedule streaming jobs" section.

질문 # 76
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of records is calculated from the Hive metastore
B. The total count of records is calculated from the parquet file metadata
C. The total count of rows is calculated by scanning all data files
D. The total count of rows will be returned from cached results unless REFRESH is run
E. The total count of records is calculated from the Delta transaction logs

정답：E

설명：
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%
20number,a%20given%20Delta%20table%20version.

질문 # 77
When using the complete mode to write stream data, how does it impact the target table?

A. Entire stream waits for complete data to write
B. Target table is overwritten for each batch
C. Stream must complete to write the data
D. Target table cannot be updated while stream is pending
E. Delta commits transaction once the stream is stopped

정답：B

설명：
Explanation
The answer is Target table is overwritten for each batch
Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries

질문 # 78
Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

A. Disable the auto-scaling feature
B. Use High concurrency mode instead of the standard mode
C. Setup multiple clusters so each team member has their own cluster
D. Increase the size of the driver node

정답：B

설명：
Explanation
The answer is Use High concurrency mode instead of the standard mode,
https://docs.databricks.com/clusters/cluster-config-best-practices.html#cluster-mode High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs.
Databricks recommends enabling autoscaling for High Concurrency clusters.

질문 # 79
A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for three consecutive days.
Ensure that notifications are not spammed during sustained issues.
Options:

A. SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated
B. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
C. SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours
D. WITH daily_avg AS (
SELECT DATE_TRUNC('DAY', window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC('DAY', window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once

정답：D

설명：
The key requirement is to detect when the daily average of percent_null is greater than 5% for three consecutive days.
Option A only checks the last 24 hours, not consecutive days. It would trigger too frequently and cause spam.
Option C calculates an average across all records in the last 3 days, but this could be skewed by one high or low day - it does not ensure consecutive daily violations.
Option D simply counts days where the threshold was exceeded, but it does not guarantee that those days were consecutive. This could incorrectly trigger on non-adjacent violations.
Option B is correct:
It aggregates hourly values into daily averages.
It checks that the last 3 consecutive days all had averages above 5%.
It avoids redundant alerts by using Notification Frequency: Just once.
This matches Databricks Lakehouse Monitoring best practices, where SQL alerts should be designed to aggregate metrics to the correct granularity (daily here) and ensure consecutive threshold violations before triggering.
Reference (Databricks Lakehouse Monitoring, SQL Alerts Best Practices):
Use DATE_TRUNC to compute metrics at the correct time granularity.
To detect consecutive-day issues, filter the last N daily aggregates and check conditions across all rows.
Always configure alerts with controlled notification frequency to prevent alert fatigue.

질문 # 80
......

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