시험준비에가장좋은Databricks-Certified-Professional-Data-Engineer인기시험최신버전덤프샘풀문제다운받기

참고: Itcertkr에서 Google Drive로 공유하는 무료 2025 Databricks Databricks-Certified-Professional-Data-Engineer 시험 문제집이 있습니다: https://drive.google.com/open?id=1R9vEPSym0e3gAVbzdOBEVxlwcGZROBqL

IT인증자격증은 여느때보다 강렬한 경쟁율을 보이고 있습니다. Databricks 인증Databricks-Certified-Professional-Data-Engineer시험을 통과하시면 취직 혹은 승진이나 연봉협상에 많은 도움이 되어드릴수 있습니다. Databricks 인증Databricks-Certified-Professional-Data-Engineer시험이 어려워서 통과할 자신이 없다구요? Itcertkr덤프만 있으면 이런 고민은 이제 그만 하지않으셔도 됩니다. Itcertkr에서 출시한 Databricks 인증Databricks-Certified-Professional-Data-Engineer덤프는 시장에서 가장 최신버전입니다.

Databricks Certified Professional Data Engineer Certification 시험을 치르려면 응시자는 데이터 엔지니어링 및 데이터 작업 경험에 대한 강력한 배경 지식을 가져야합니다. 이 시험은 객관식 질문과 실습 작업으로 구성되어 후보자가 데이터 사업을 사용하여 데이터 처리 시스템을 설계, 구축 및 관리하는 능력을 보여 주어야합니다. 시험은 데이터 수집, 데이터 변환, 데이터 저장, 데이터 처리 및 데이터 시각화를 포함한 광범위한 주제를 다룹니다. 시험이 성공적으로 완료되면 응시자는 Databricks Certified Professional Data Engineer Certification을 받게되며, 이는 데이터 처리 시스템을 구축하고 유지 관리하는 데 대한 전문 지식을 보여줍니다.

이 시험은 후보자의 Databricks를 실제 업무 환경에서 사용하는 능력을 시험하는 것을 목적으로 합니다. 후보자들은 확장 가능하고 효율적이며 신뢰성이 뛰어난 데이터 파이프라인을 설계하고 구현할 수 있는 능력을 증명해야 합니다. 또한 데이터 엔지니어링 프로세스 중 발생하는 문제를 해결하고 파이프라인이 원활하게 실행되도록 성능을 최적화해야 합니다.

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Databricks-Certified-Professional-Data-Engineer인기시험 최신 인기시험 공부문제

Itcertkr덤프를 IT국제인증자격증 시험대비자료중 가장 퍼펙트한 자료로 거듭날수 있도록 최선을 다하고 있습니다. Databricks Databricks-Certified-Professional-Data-Engineer 덤프에는Databricks Databricks-Certified-Professional-Data-Engineer시험문제의 모든 범위와 유형을 포함하고 있어 시험적중율이 높아 구매한 분이 모두 시험을 패스한 인기덤프입니다.만약 시험문제가 변경되어 시험에서 불합격 받으신다면 덤프비용 전액 환불해드리기에 안심하셔도 됩니다.

Databricks Certified Professional Data Engineer 시험에 통과하는 것은 빅데이터와 클라우드 컴퓨팅 분야에서 경력을 쌓고자 하는 개인들에게 중요한 성취입니다. 이 자격증은 후보자가 Apache Spark에 대한 깊은 이해를 가지고 이를 적용하여 확장 가능한 빅데이터 솔루션을 설계하고 구현할 수 있다는 것을 입증합니다. 또한, 이 자격증은 많은 기업에서 인정되어 후보자의 고수익 취업 기회를 향상시킬 수 있습니다.

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q107-Q112):

질문 # 107
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?

A. There is no column in the table named heartrateheartrateheartrate.
B. There is a type error because a DataFrame object cannot be multiplied.
C. There is a type error because a column object cannot be multiplied.
D. There is a syntax error because the heartrate column is not correctly identified as a column.

정답：A

설명：
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "select() expects column names or Column expressions."
* Exact extract: "When using strings directly, Spark SQL interprets them as literal column names."
* Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate".
The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.
References: PySpark DataFrame select; PySpark Column expressions with col().

질문 # 108
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?

A. 1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
B. 1.(spark.table("sales").groupBy("store")
2..agg(sum("sales")).writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
C. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
D. 1.(spark.table("sales").agg(sum("sales"),sum("units"))
2..writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
E. 1.(spark.table("sales")
2..withColumn("avgPrice", col("sales") / col("units"))
3..writeStream
4..option("checkpointLocation", checkpointPath)
5..outputMode("append")
6..table("cleanedSales"))

정답：E

설명：
Explanation
A diagram of a house Description automatically generated with low confidence

질문 # 109
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
B. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
D. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
E. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

정답：D

설명：
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

질문 # 110
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
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.

정답：D

설명：
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.
References:
* Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta
/optimizations/auto-optimize.html

질문 # 111
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. 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
B. 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
C. 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
D. 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

정답：A

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 # 112
......

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