A data engineering team ingests large Parquet files from an S3 bucket into a Delta table. The files are partitioned by 'date' and 'region', but the current ingestion job is slow because it processes files sequentially. The team wants to improve throughput without changing the source data. Which approach should they use?

Increase the number of Spark executors and set the 'spark.sql.files.maxPartitionBytes' option to a larger value.

Repartition the data to a single partition before writing to Delta.

Use Auto Loader to ingest the files instead of a batch read.

Use the 'spark.sql.shuffle.partitions' option set to 1 to reduce shuffle overhead.

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 2)

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.
  ‹123 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 6–10

6application · medium
A data engineering team ingests large Parquet files from an S3 bucket into a Delta table. The files are partitioned by 'date' and 'region', but the current ingestion job is slow because it processes files sequentially. The team wants to improve throughput without changing the source data. Which approach should they use?
Select an answer first
7foundation · easy
A data engineer needs to store large analytical datasets that are frequently queried with column-level aggregations. Which format should they choose to optimize query performance and storage efficiency?
Select an answer first
8foundation · easy
Which of the following is a valid cloud storage source for reading data into Spark in Databricks?
Select an answer first
9foundation · easy
Which Databricks feature is specifically designed to incrementally ingest new files from cloud storage like S3 or ADLS without requiring a separate streaming cluster?
Select an answer first
10foundation · easy
Which Delta Lake ingestion method is best suited for a one-time or scheduled batch load of a specific set of files, ensuring that re-running the load does not duplicate data?
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 2)

Questions 6–10

A data engineer needs to store large analytical datasets that are frequently queried with column-level aggregations. Which format should they choose to optimize query performance and storage efficiency?

Which of the following is a valid cloud storage source for reading data into Spark in Databricks?

Which Databricks feature is specifically designed to incrementally ingest new files from cloud storage like S3 or ADLS without requiring a separate streaming cluster?

Which Delta Lake ingestion method is best suited for a one-time or scheduled batch load of a specific set of files, ensuring that re-running the load does not duplicate data?

Keep studying — Data Ingestion & Acquisition