Databricks Certified Data Engineer - Professional

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Explanation:

The correct answer is D.

Explanation:

By default, Databricks Auto Loader operates in Directory Listing mode. In this mode, Auto Loader identifies new files by periodically scanning the input directory and tracking the processed files in a checkpoint. This allows for incremental and idempotent ingestion (ensuring each file is processed exactly once) without requiring complex cloud-native configurations.

Why the other options are incorrect:

Option A: This describes File Notification mode. While often more scalable for directories with millions of files, it is not the default and must be explicitly enabled (e.g., by setting cloudFiles.useNotifications to true).
Option B: Auto Loader does not use webhooks to trigger jobs; it is integrated into Spark Structured Streaming and relies on its own discovery mechanisms (listing or notifications).
Option C: Auto Loader is designed specifically to avoid full directory scans once it has established a state. It tracks discovered files to ensure that it only processes new data rather than recreating the table from scratch on every run.

Explanation:

The correct answer is D.

Explanation:

Why the other options are incorrect:

Option A: This describes File Notification mode. While often more scalable for directories with millions of files, it is not the default and must be explicitly enabled (e.g., by setting cloudFiles.useNotifications to true).
Option B: Auto Loader does not use webhooks to trigger jobs; it is integrated into Spark Structured Streaming and relies on its own discovery mechanisms (listing or notifications).
Option C: Auto Loader is designed specifically to avoid full directory scans once it has established a state. It tracks discovered files to ensure that it only processes new data rather than recreating the table from scratch on every run.

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Which statement accurately describes the default execution mode used by Databricks Auto Loader to detect and process new files?

Real Exam

Last updated: January 6, 2026 at 15:42

It utilizes cloud-specific notification services (e.g., AWS SNS/SQS or Azure Event Grid) and queue storage to track and incrementally load new files.

4.8%

It relies on webhooks to automatically trigger Databricks jobs whenever new files arrive, merging data into target tables using schema inference.

9.5%

It performs a full scan of the source directory during every trigger, recreating the target Delta table by querying all valid files found in the source.

9.5%

It scans the input directory to identify new files, loading them incrementally and idempotently into the target Delta Lake table.

76.2%