Databricks-Certified-Data-Engineer-Professional復習解答例 & Databricks-Certified-Data-Engineer-Professionalテスト参考書

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

Section	Objectives
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Delta Lake and Data Management	- Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties
Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold)
Production Pipelines and Orchestration	- Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring

>> Databricks-Certified-Data-Engineer-Professional復習解答例 <<

Databricks-Certified-Data-Engineer-Professionalテスト参考書、Databricks-Certified-Data-Engineer-Professional試験関連情報

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q144-Q149):

質問 # 144
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. overwriteSchema = true
D. ignoreChanges = false

正解：B

解説：
Setting mergeSchema to true allows Delta Lake to automatically evolve the table schema by incorporating new columns from the source data during writes. This enables append-only pipelines to handle schema changes seamlessly in both batch and streaming workloads without breaking the pipeline.

質問 # 145
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 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.
B. 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.
C. 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.
D. 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.
E. 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.

正解：D

質問 # 146
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
B. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
C. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
D. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.

正解：B

解説：
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.

質問 # 147
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
B. Rewrite their code to avoid putting memory pressure on the driver node.
C. Run the notebook on a single node cluster to keep driver from falling.
D. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.

正解：B

解説：
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

質問 # 148
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

正解：C

解説：
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

質問 # 149
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