Databricks Data Engineer Associate · 6% practice allocation

Databricks Data Engineer Associate: Databricks Intelligence Platform Practice Questions

5 free sample questions from the Databricks Intelligence Platform domain, with answers and explanations. The full Data Engineer Associate bank has 300 questions across 7 domains. Questions are scenario-based and written from the official Databricks exam guide.

1.

Which compute option is purpose-built for running SQL queries and BI dashboards efficiently?

  • A. The driver's local disk
  • B. A Delta Sharing share
  • C. A SQL warehouse✓ Correct
  • D. A single-node all-purpose cluster with no SQL support
Explanation

SQL warehouses are optimized compute for SQL analytics and BI. The other options aren't SQL-serving compute.

2.

A data engineer at a financial services company needs to implement a data retention policy requiring that all historical versions of a Delta Lake table older than 7 days are permanently purged to comply with data privacy regulations. The engineer plans to run VACUUM with a 7-day retention threshold. Which of the following considerations should the engineer be aware of before running this command? (Select TWO)

  • A. VACUUM with a retention of 7 days will also remove the current version's data files if they were written more than 7 days ago.
  • B. VACUUM will delete the _delta_log transaction log files for all commits older than 7 days, making time travel to versions within the 7-day window impossible.
  • C. VACUUM automatically creates a backup of all deleted files in a Databricks-managed recovery bucket before deletion.
  • D. Running VACUUM will permanently delete data files no longer referenced by the current table version that are older than the retention threshold, making time travel to those versions impossible.✓ Correct
  • E. Before running VACUUM, active readers or writers with open transactions older than the retention threshold may encounter errors if their referenced files are deleted.✓ Correct
Explanation

VACUUM deletes unreferenced Parquet data files older than the retention period, permanently removing the ability to time travel to those versions. Additionally, any long-running reader that holds a reference to files older than the retention window may fail after VACUUM completes, because those files no longer exist. Option B is wrong; VACUUM does not delete transaction log (_delta_log) JSON or checkpoint files — it only deletes orphaned data files. Option C is incorrect; Databricks does not automatically back up vacuumed files. Option A is wrong; VACUUM only deletes files NOT referenced by the current or any retained version — files that are part of the current table state are never deleted by VACUUM regardless of their age.

3.

In the medallion architecture, which layer holds raw, minimally processed ingested data?

  • A. Gold
  • B. Silver
  • C. Bronze✓ Correct
  • D. Platinum
Explanation

Bronze holds raw ingested data; silver holds cleaned/conformed data; gold holds business-level aggregates. There is no standard 'platinum' layer (D).

4.

A data engineer is onboarding to a Databricks workspace and wants to understand which components are managed by Databricks in its cloud account versus which components run inside the customer's cloud account. Which of the following correctly identifies a component that lives in the Databricks-managed CONTROL PLANE?

  • A. The Databricks web application, REST API endpoints, and job scheduler✓ Correct
  • B. The Apache Spark driver and executor processes running on cluster VMs
  • C. The customer's cloud object storage buckets containing data
  • D. The raw Parquet files that back a Delta Lake table
  • E. The cluster network interfaces and VPC/VNet where worker nodes run
Explanation

Option A is correct: the Databricks web application (UI), REST APIs, job scheduler, cluster manager, and notebook server are all hosted in the Databricks-managed control plane in Databricks' own cloud account. Option B is wrong: the Spark driver and executor processes run on cluster VMs in the customer's data plane (the customer's cloud account). Option D is wrong: raw Parquet/Delta files reside in the customer's cloud object storage, which is part of the data plane. Option C is wrong: cloud object storage buckets are customer-owned and part of the data plane. Option E is wrong: cluster VMs, their network interfaces, and the VPC/VNet are provisioned in the customer's cloud account as part of the data plane.

5.

A data engineer is designing a Delta Lake table for an event logging system. The team inserts millions of small individual event records throughout the day using streaming micro-batches, resulting in thousands of tiny Parquet files accumulating over several weeks. Which of the following approaches BEST addresses this small-file problem while maintaining Delta Lake capabilities? (Select TWO)

  • A. Convert the Delta table to CSV format, which inherently avoids small-file overhead by appending to a single file.
  • B. Enable Auto Optimize (Auto Compaction and Optimized Writes) on the table so that Delta Lake automatically compacts small files during and after write operations.✓ Correct
  • C. Increase the number of streaming micro-batch partitions to 10,000 to distribute the small files more evenly across worker nodes.
  • D. Run VACUUM with a retention of 0 hours to immediately delete all small files and force Delta Lake to regenerate consolidated files.
  • E. Run the OPTIMIZE command on the table periodically (e.g., nightly), which rewrites small files into larger, right-sized Parquet files.✓ Correct
Explanation

Correct: (E) The OPTIMIZE command explicitly compacts small Parquet files into larger target-size files (default ~1 GB), directly solving the small-file problem. It can be scheduled to run periodically without disrupting reads. (B) Auto Optimize combines Optimized Writes (which reduces the number of files written per operation) and Auto Compaction (which compacts files after writes), proactively preventing and reducing small-file accumulation without manual scheduling. Wrong: (A) Converting to CSV would lose all Delta Lake features (ACID, time travel, schema enforcement) and CSV does not append to a single file—it creates many files just like Delta. (D) Running VACUUM with 0-hour retention deletes old data files but does NOT consolidate or compact existing small files; it would also bypass the default 7-day safety threshold and could break time travel. (C) Increasing the number of partitions would create MORE small files, not fewer—this worsens the problem.

Test yourself across every Data Engineer Associate domain

The free Data Engineer Associate practice test mixes about 20 questions weighted like the exam. The full 300-question bank adds instant grading, flashcards and a timed mock exam.

Study this domain

Databricks Intelligence Platform study guide (Data Engineer Associate) →

Other Data Engineer Associate domains

← All Databricks Data Engineer Associate study resources