최신버전Databricks-Certified-Data-Engineer-Professional최고품질인증시험기출자료덤프샘플문제다운로드

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Delta Lake - Unity Catalog
Topic 2: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Topic 3: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 4: Data Processing	28%	- ETL Pipelines - Data Transformation - Spark SQL - Structured Streaming
Topic 5: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage

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Databricks-Certified-Data-Engineer-Professional최고품질 인증시험 기출자료 시험대비 덤프자료

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q64-Q69):

질문 # 64
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
D. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
E. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.

정답：D

설명：
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.

질문 # 65
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.files.maxPartitionBytes
B. spark.sql.adaptive.coalescePartitions.minPartitionNum
C. spark.sql.adaptive.advisoryPartitionSizeInBytes
D. spark.sql.files.openCostInBytes
E. spark.sql.autoBroadcastJoinThreshold

정답：A

설명：
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

질문 # 66
A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A. Configure Structured Streaming with dropDuplicates transformation
B. Use Delta Lake time travel to identify and remove duplicates
C. Implement a write operation using MERGE INTO with a unique key
D. Use Delta Lake's change data feed to filter duplicate records

정답：C

설명：
Using MERGE INTO with a unique key enforces idempotent writes at the Delta Lake table level.
This approach reliably handles duplicates even when events arrive far outside any streaming watermark, ensuring that each logical record is written exactly once regardless of arrival time.

질문 # 67
What is the first line of a Databricks Python notebook when viewed in a text editor?

A. # MAGIC %python
B. // Databricks notebook source
C. -- Databricks notebook source
D. # Databricks notebook source
E. %python

정답：D

설명：
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

질문 # 68
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
B. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
C. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
D. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
E. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

정답：A

질문 # 69
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