최신Databricks-Certified-Data-Engineer-Associate시험대비덤프데모문제시험덤프문제

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

Section	Objectives
Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics
Data Processing and Transformations	- Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations - User-defined functions (UDFs)
Data Ingestion and ELT Development	- Handling structured and semi-structured data - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark
Productionizing Data Pipelines	- Scheduling and monitoring jobs - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts

>> Databricks-Certified-Data-Engineer-Associate시험대비 덤프데모문제 <<

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Associate 무료샘플문제 (Q227-Q232):

질문 # 227
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

정답：A

질문 # 228
Which query is performing a streaming hop from raw data to a Bronze table?

정답：A

설명：
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
Reference:
Databricks documentation on Structured Streaming: Structured Streaming in Databricks

질문 # 229
A data engineer is tasked with cleaning a bronze table. The requirement is to eliminate rows where either the customer_email or the customer_phone field is null. This data cleaning must be performed in a single operation using a single method call. Which PySpark approach supports filtering multiple columns for nulls in one call?

A. df.na.drop(how='all')
B. df.where('customer_email IS NOT NULL').where('customer_phone IS NOT NULL')
C. df.dropna(subset=['customer_email', 'customer_phone'])
D. df.filter(col('customer_email').isNotNull() & col('customer_phone').isNotNull())

정답：C

설명：
df.dropna(subset=['customer_email', 'customer_phone']) uses one method call and, by default, removes rows where either specified column is null.

질문 # 230
A data engineer is reviewing the documentation on audit logs in Databricks for compliance purposes and needs to understand the format in which audit logs output events.
How are events formatted in Databricks audit logs?

A. In Databricks, audit logs output events in a CSV format.
B. In Databricks, audit logs output events in an XML format.
C. In Databricks, audit logs output events in a plain text format. In Databricks, audit logs output events in a JSON format.

정답：B

질문 # 231
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

A. JDBC data source
B. Driver node
C. Databricks Filesystem
D. Databricks web application
E. Worker node

정답：D

설명：
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network.
References: Databricks architecture overview, Security and Trust Center

질문 # 232
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