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DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE · Question #15

A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will…

The correct answer is E. Auto Loader. Auto Loader is the appropriate tool for incrementally identifying and ingesting only new files from a continuously accumulating shared directory.

Submitted by jian89· Apr 18, 2026ELT with Spark SQL and Python

Question

A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run. Which of the following tools can the data engineer use to solve this problem?

Options

  • AUnity Catalog
  • BDelta Lake
  • CDatabricks SQL
  • DData Explorer
  • EAuto Loader

How the community answered

(50 responses)
  • A
    14% (7)
  • B
    10% (5)
  • C
    2% (1)
  • D
    4% (2)
  • E
    70% (35)

Why each option

Auto Loader is the appropriate tool for incrementally identifying and ingesting only new files from a continuously accumulating shared directory.

AUnity Catalog

Unity Catalog provides a unified governance layer for data and AI assets, not a mechanism for incremental file ingestion from directories.

BDelta Lake

Delta Lake is an open-source storage layer that brings ACID transactions to data lakes, but it does not directly handle the incremental identification of new files in a directory.

CDatabricks SQL

Databricks SQL is an environment for running SQL queries and dashboards, not a tool for managing file ingestion pipelines.

DData Explorer

Data Explorer is a UI tool within Databricks for browsing and managing data objects, not for building data ingestion pipelines.

EAuto LoaderCorrect

Auto Loader is specifically designed to efficiently ingest new data files as they arrive in cloud storage, providing exactly-once processing guarantees and automatically tracking processed files without manual state management, which perfectly fits the described scenario.

Concept tested: Auto Loader for incremental ingestion

Source: https://docs.databricks.com/en/ingestion/auto-loader/index.html

Topics

#Auto Loader#Incremental data ingestion#Structured Streaming#File processing

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