Databricks Data Engineer Professional · 7% practice allocation

Databricks Data Engineer Professional: Data Ingestion and Acquisition Practice Questions

5 free sample questions from the Data Ingestion and Acquisition domain, with answers and explanations. The full Data Engineer Professional bank has 311 questions across 10 domains. Questions are scenario-based and written from the official Databricks exam guide.

New JSON files land continuously in a cloud storage location and must be ingested incrementally with automatic detection of new files and schema evolution. Which Databricks feature is designed for this?

A. Auto Loader (cloudFiles)✓ Correct
B. COPY INTO run once manually
C. A static batch read of the whole directory each hour
D. Delta Sharing

Explanation

Auto Loader (cloudFiles) incrementally and efficiently detects and ingests new files as they arrive, with schema inference/evolution and scalable file discovery. A one-time COPY INTO (B) isn't continuous, a full directory batch read (C) reprocesses everything, and Delta Sharing (D) shares data across orgs.

How does Auto Loader efficiently track which files it has already ingested at very large scale?

A. It stores state only in the driver's memory
B. It relies on the file names being sequential integers
C. It maintains its ingestion state in a checkpoint (and can use file notification/directory listing), so already-processed files aren't reprocessed✓ Correct
D. It rereads every file in the directory each run

Explanation

Auto Loader records processed files in its checkpoint (using scalable file discovery, including file notifications), so it ingests only new files and scales to millions of files. Rereading all files (D) doesn't scale, sequential names (B) aren't required, and driver memory (A) wouldn't survive restarts.

A team needs an idempotent batch ingestion command that will not re-ingest files it has already loaded into a Delta table. Which command fits?

A. COPY INTO (which tracks loaded files and skips duplicates)✓ Correct
B. INSERT INTO run repeatedly with no tracking
C. A plain spark.read of the folder each time
D. MERGE with no condition

Explanation

COPY INTO is idempotent — it records which files it has loaded and skips them on re-runs, making retries safe. Repeated INSERT INTO (B) or a folder read (C) would duplicate data, and a conditionless MERGE (D) isn't an ingestion-dedup mechanism.

For a streaming ingestion pipeline, what MUST be configured so the stream can recover after a failure and continue exactly-once?

A. A checkpoint location for the streaming query✓ Correct
B. A larger driver node only
C. A ZORDER on the source
D. A Delta Sharing recipient

Explanation

A checkpoint location persists the stream's offsets and state, enabling fault-tolerant recovery and exactly-once semantics. A bigger driver (B), ZORDER (C), and Delta Sharing recipients (D) don't provide streaming recovery.

Auto Loader encounters files whose schema has an added field not seen before. With schema evolution enabled, what happens?

A. It deletes the target table
B. It silently drops all data
C. Auto Loader can evolve the schema to include the new column (and may restart the stream to pick it up), rather than failing✓ Correct
D. It ignores schema entirely and stores raw bytes

Explanation

With schema evolution, Auto Loader adds newly detected columns to the schema (often restarting the stream to apply the change) instead of failing on the mismatch. It doesn't drop data (B), delete the table (A), or store raw bytes ignoring schema (D).

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