10 Databricks Data Engineer Associate Practice Questions That Mirror the Real Exam
Here are 10 multiple-choice questions designed to mirror the style, difficulty, and domain coverage of the Databricks Data Engineer Associate exam. The questions focus on the Delta Lake architecture, Medallion architecture, optimization techniques, and Spark/Databricks specific operations.
Instructions: Choose the best possible answer for each question. These questions simulate the conceptual and scenario-based problems you will face on the actual certification. For a deeper dive, check out How Hard Is Databricks Data Engineer Associate, Really? An Honest Difficulty Rating.
Question 1
A data engineer is designing a bronze table to ingest raw JSON events from a streaming source. They need to ensure that the table can handle schema evolution without breaking the ingestion pipeline. Which Databricks feature should they enable to allow new fields to be added to the JSON payloads automatically?
A. Delta Lake Change Data Feed (CDF)
B. Delta Lake Schema Enforcement
C. Delta Lake Schema Evolution
D. Autoloader file notification mode
Answer: C
Explanation: Schema Evolution (using mergeSchema option) allows new columns to be added to the Delta table automatically when new JSON fields appear. Schema Enforcement (B) would reject the data, while CDF (A) is for tracking row-level changes, and Autoloader (D) is the ingestion tool but not the specific feature for altering the table schema.
Question 2
A Delta Lake table has been running for months and has thousands of small files. A query filtering on a highly selective customer_id column is running slowly. Which optimization technique is most likely to improve the query performance by physically co-locating data with the same customer_id?
A. OPTIMIZE with ZORDER BY (customer_id)
B. VACUUM to remove old files
C. MERGE to upsert the data
D. ANALYZE TABLE to update statistics
Answer: A
Explanation: ZORDER BY creates a data layout that co-locates similar values in the same files, reducing the number of files scanned for selective queries. VACUUM (B) cleans up unused files, MERGE (C) is for data modification, and ANALYZE TABLE (D) updates stats but does not physically reorganize data.
Question 3
You are working in a Databricks notebook and have a DataFrame sales_df with a column order_date (timestamp type). You need to create a new column order_week that contains the week of the year for each date. Which function should you use?
A. date_format(order_date, "w")
B. weekofyear(order_date)
C. trunc(order_date, "week")
D. datediff(order_date, "week")
Answer: B
Explanation: The weekofyear function returns the week number (1-53) of the year for a given timestamp. date_format (A) returns a string, trunc (C) truncates to the start of the week, and datediff (D) calculates the difference between two dates, not a week number.
Question 4
A pipeline uses the Medallion Architecture. The Silver layer is designed to perform data cleaning and deduplication. Which of the following operations is most characteristic of the transition from Bronze to Silver?
A. Joining the data with multiple lookup tables to create a wide, business-ready table.
B. Applying quality checks, parsing the raw data, and removing duplicate records. For practical tips on this, see Databricks Data Engineer Associate Exam Day: What the First Ten Minutes Look Like.
C. Aggregating daily sales totals for a dashboard.
D. Storing the raw, unmodified JSON payloads for audit purposes.
Answer: B
Explanation: The Silver layer is where data is cleansed, validated, and conformed (deduplicated, parsing, etc.) to create a trusted, queryable source. Option A describes the Gold layer (business-specific), C describes Gold layer aggregations, and D describes the Bronze layer (raw ingestion).
Question 5
You have a Delta table named events. You need to delete all rows where event_type = 'click' and user_id = 123. Which command accomplishes this?
A. DELETE FROM events WHERE event_type = 'click' AND user_id = 123
B. REMOVE FROM events WHERE event_type = 'click' AND user_id = 123
C. UPDATE events SET status = 'deleted' WHERE event_type = 'click' AND user_id = 123
D. DROP TABLE events
Answer: A
Explanation: Delta Lake supports standard SQL DELETE with a WHERE clause to remove rows that match conditions. REMOVE is not a valid Delta command, UPDATE (C) modifies values, and DROP TABLE (D) removes the entire table schema and data.
Question 6
A Databricks Job is scheduled to run a notebook every hour. The notebook writes data to a Delta table using df.write.mode("overwrite"). The data engineer notices that the table size grows too large. What is the most likely cause?
A. The overwrite mode does not delete old files, it only appends new ones.
B. The table is not being OPTIMIZEd, causing small files to accumulate.
C. The overwrite mode is creating a new version of the table, and VACUUM is not run, so old files remain on disk.
D. The notebook is writing to the wrong table path.
Answer: C
Explanation: overwrite creates a new table version, but old files are not physically deleted until VACUUM is run. While small files (B) can be an issue, the primary cause of disk growth with frequent overwrites is the accumulation of old versions that haven't been vacuumed. Option A is incorrect; overwrite does replace files logically.
Question 7
Which of the following tools is specifically designed for incrementally processing new files as they land in cloud object storage (e.g., S3 or ADLS) without needing to manually track which files have been processed?
A. Structured Streaming with readStream
B. Databricks SQL
C. Delta Live Tables (DLT)
D. Auto Loader (cloudFiles)
Answer: D Want to go further? 10 Google Associate Cloud Engineer Practice Questions That Mirror the Real Exam is a good next step.
Explanation: Auto Loader (or cloudFiles) is the recommended Databricks solution for incrementally discovering and processing new data files in object storage. While Structured Streaming (A) can read from files, Auto Loader handles schema inference and file tracking more robustly. DLT (C) is an orchestration framework, and Databricks SQL (B) is for querying, not ingesting.
Question 8
You are performing a MERGE (upsert) operation on a large fact table. The source table is small, containing only the recent changes. Which of the following best describes how Delta Lake performs this operation under the hood?
A. It rewrites the entire table to ensure all data is consistent.
B. It only rewrites the files that contain the matching keys from the source data.
C. It appends the new data to the end of the table and deletes the old data.
D. It creates a temporary view and swaps the table pointer.
Answer: B
Explanation: Delta Lake's MERGE is optimized to only rewrite the files that contain the records being updated or deleted. This is a key performance advantage over rewriting the entire table (A). C describes a simple append and delete, and D describes a table swap, which is not how Delta MERGE works.
Question 9
A data engineer needs to share a cleaned and aggregated dataset with a business intelligence (BI) tool. The data is stored in a Delta Lake table. Which of the following is the most efficient way to query this data from the BI tool using Databricks?
A. Use the Databricks SQL Warehouse (formerly SQL Endpoint) to run queries against the Delta table.
B. Export the table to a CSV file and upload it to the BI tool.
C. Have the BI tool connect directly to the underlying Parquet files in cloud storage.
D. Use a Python script in a notebook to loop through the data and send it to the BI tool's API.
Answer: A
Explanation: Databricks SQL Warehouses are optimized for serving high-performance SQL queries on Delta Lake tables to BI tools. Exporting to CSV (B) is inefficient, reading Parquet directly (C) bypasses Delta's transaction log and optimization, and using a Python loop (D) is not scalable.
Question 10
You are writing a streaming job using Structured Streaming to read from a Delta table and write to another Delta table. You want to ensure the streaming job only processes new changes since the last run and does not reprocess the entire table. What is the correct way to achieve this?
A. Use spark.readStream.table("table_name") and write with append mode.
B. Use spark.read.table("table_name") and write with overwrite mode.
C. Use spark.readStream.table("table_name") and write with complete mode.
D. Use spark.sql("SELECT * FROM table_name") in a scheduled job.
Answer: A
Explanation: Reading a Delta table using readStream automatically leverages the Delta Lake transaction log to track the offset, enabling incremental processing. Writing with append mode adds new rows to the target table. Option B is a batch read, C (complete mode) rewrites the entire output, and D is a batch query.
These questions cover the core concepts of Delta Lake operations, Medallion architecture, performance tuning, and streaming ingestion. To succeed on the real exam, ensure you understand why the correct answers are right, not just memorize the answers.
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