How does the Spark XML library handle XML attributes when reading data?

Attributes are ignored by default.

Attributes are exposed as columns prefixed with an underscore.

Attributes are merged into the element's text content.

Attributes are stored in a separate map column.

What is the default behavior of Delta Lake when a DataFrame write contains a column that is not present in the target table's schema?

The extra column is silently dropped.

The extra column is added to the transaction log but not the data.

The schema is automatically evolved to include the new column.

A data engineering team ingests data from multiple S3 buckets into a single Delta table. Each bucket contains files in a different format (JSON, CSV, Parquet). The team wants to use Auto Loader to incrementally ingest all files, but they are concerned about schema consistency across the different formats. Which approach should they use?

Use a single Auto Loader stream with the 'cloudFiles.format' option set to 'json'.

Use COPY INTO for each bucket and union the results.

Use a single Auto Loader stream with schema inference enabled.

Use a separate Auto Loader stream for each bucket, with a common schema, and union the results before writing to Delta.

DatabricksCertified Data Engineer Professional

Domain 2Objective 1

Design and Implement Data Ingestion Pipelines to Efficiently Ingest a Variety of Data Formats Including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from Diverse Sources Such as Message Buses and Cloud Storage. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 5)

Part of the Data Ingestion & Acquisition domain, which accounts for 7% of the DATA-ENGINEER-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~4–6 in this domain), expect 2–3 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

15concepts

7%of the exam

Curriculum map

Developing Code for Data Processing using Python and SQL
Data Ingestion & Acquisition
- Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.
  ‹1 456 7 ›
- Create an append-only data pipeline capable of handling both batch and streaming data using Delta.
Data Transformation, Cleansing, and Quality
Data Sharing and Federation
Monitoring and Alerting
5 more domains

15 Concepts in This Objective

Data Format Characteristics
Format Selection Criteria
Delta Lake Ingestion
Parquet and ORC Ingestion
Avro Ingestion
JSON Ingestion
CSV Ingestion
XML Ingestion
Text Ingestion
Binary Ingestion
Message Bus Ingestion
Cloud Storage Ingestion
Incremental Ingestion Patterns
Schema and Data Validation
Performance Optimization for Ingestion

Questions 21–25

21foundation · easy
How does the Spark XML library handle XML attributes when reading data?
Select an answer first
22foundation · easy
What is the default behavior of Delta Lake when a DataFrame write contains a column that is not present in the target table's schema?
Select an answer first
23foundation · easy
Which option in the Spark XML library specifies the XML element that should be treated as a row in the DataFrame?
Select an answer first
24expert · hard
A data engineering team ingests data from multiple S3 buckets into a single Delta table. Each bucket contains files in a different format (JSON, CSV, Parquet). The team wants to use Auto Loader to incrementally ingest all files, but they are concerned about schema consistency across the different formats. Which approach should they use?
Select an answer first
25foundation · easy
Which format is most appropriate when the primary requirement is to support schema evolution and maintain backward/forward compatibility in a streaming data pipeline?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to DATA-ENGINEER-PROFESSIONAL

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Databricks. “DATA-ENGINEER-PROFESSIONAL” is a trademark of its owner, used for identification only.

Design and Implement Data Ingestion Pipelines to Efficiently Ingest a Variety of Data Formats Including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from Diverse Sources Such as Message Buses and Cloud Storage. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 5)

Questions 21–25

How does the Spark XML library handle XML attributes when reading data?

What is the default behavior of Delta Lake when a DataFrame write contains a column that is not present in the target table's schema?

Which option in the Spark XML library specifies the XML element that should be treated as a row in the DataFrame?

Which format is most appropriate when the primary requirement is to support schema evolution and maintain backward/forward compatibility in a streaming data pipeline?

Keep studying — Data Ingestion & Acquisition