A data platform team is designing a new ingestion pipeline for a large dataset of user events. The data will be used for both real-time dashboards and historical analytics. The team needs to choose a storage format for the raw ingested data that balances query performance, storage efficiency, and schema evolution support. Which format should they choose?

Parquet with snappy compression.

Delta Lake with Parquet as the underlying format.

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 3)

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.
  ‹1 234 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 11–15

11foundation · easy
Which component is commonly used to manage Avro schemas and provide schema registry functionality in a streaming data pipeline?
Select an answer first
12foundation · easy
Which component in Structured Streaming stores the progress of processed data to enable fault-tolerant incremental processing?
Select an answer first
13foundation · easy
Which Spark API is used to ingest streaming data from a message bus like Kafka into a DataFrame for continuous processing?
Select an answer first
14foundation · easy
Which data format is row-based and stores the schema as JSON within each file, making it well-suited for systems that need schema evolution and compatibility?
Select an answer first
15application · medium
A data platform team is designing a new ingestion pipeline for a large dataset of user events. The data will be used for both real-time dashboards and historical analytics. The team needs to choose a storage format for the raw ingested data that balances query performance, storage efficiency, and schema evolution support. Which format should they choose?
Select an answer first

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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 3)

Questions 11–15

Which component is commonly used to manage Avro schemas and provide schema registry functionality in a streaming data pipeline?

Which component in Structured Streaming stores the progress of processed data to enable fault-tolerant incremental processing?

Which Spark API is used to ingest streaming data from a message bus like Kafka into a DataFrame for continuous processing?

Which data format is row-based and stores the schema as JSON within each file, making it well-suited for systems that need schema evolution and compatibility?

Keep studying — Data Ingestion & Acquisition