Databricks Certified Data Engineer Associate Practice Exam – Exam Sage
What is this exam?
The Databricks Certified Data Engineer Associate exam is a professional certification designed to validate your skills in building, managing, and optimizing data pipelines on the Databricks platform. It focuses on your ability to use Apache Spark and Delta Lake to efficiently process large datasets, implement data ingestion and transformation, and maintain reliable, scalable data workflows. This certification is ideal for data engineers looking to demonstrate expertise in modern cloud-based data engineering using Databricks.
What will you learn?
By preparing with Exam Sage’s practice exam, you will gain a deep understanding of critical data engineering concepts and hands-on skills, including:
Designing and building efficient ETL pipelines using Databricks and Apache Spark
Managing Delta Lake tables with schema enforcement, schema evolution, and data versioning
Utilizing Databricks Auto Loader for scalable, incremental data ingestion
Implementing performance optimizations like partitioning, caching, and data skipping (ZORDER)
Handling streaming and batch data processing with fault tolerance and exactly-once guarantees
Applying security best practices for data governance and access controls within Databricks
Topics covered in this practice exam:
Apache Spark Core and Spark SQL fundamentals
Delta Lake architecture, ACID transactions, and time travel
Structured Streaming and Auto Loader for streaming ingestion
Data transformation and optimization techniques
Managing data pipelines at scale
Performance tuning strategies for Delta tables
Security and governance using Unity Catalog and role-based access control
Why choose Exam Sage?
Exam Sage offers a comprehensive, up-to-date practice exam tailored specifically for the Databricks Certified Data Engineer Associate certification. Each question is crafted by subject matter experts and includes detailed explanations to reinforce your learning and help you identify areas to improve. Our user-friendly platform mimics the real exam environment, enabling you to track your progress and build confidence before the actual test. With Exam Sage, you get reliable practice that prepares you thoroughly to pass on your first attempt.
Start your journey to becoming a certified Databricks Data Engineer with Exam Sage’s trusted practice tests today!
Sample Questions and Answers
1. What is the default file format used when writing a Delta table in Databricks?
A. Parquet
B. JSON
C. Delta
D. CSV
Answer: C. Delta
Explanation:
In Databricks, when you create a Delta table without explicitly specifying a format, it defaults to the Delta Lake format. Delta is a transactional storage layer built on top of Apache Parquet that provides ACID compliance and schema enforcement. The Delta format ensures consistency and enables time travel, scalable metadata handling, and efficient updates and deletes. While Parquet is the underlying file format, Delta is the format used for tables in Delta Lake.
2. Which command is used to create a Delta table from a DataFrame in PySpark?
A. df.write.format("parquet").saveAsTable("table_name")
B. df.write.saveAsTable("table_name")
C. df.write.format("delta").saveAsTable("table_name")
D. df.saveAsTable("delta_table")
Answer: C. df.write.format("delta").saveAsTable("table_name")
Explanation:
To create a Delta table from a DataFrame in PySpark, you must specify the format explicitly using .format("delta") before calling .saveAsTable(). This command ensures that the data is written in Delta format and is registered as a managed table in the metastore. Without specifying “delta”, the table might default to other formats like Parquet depending on configurations.
3. In Delta Lake, which operation is used to maintain performance by removing obsolete data files?
A. VACUUM
B. CLEAN
C. PURGE
D. TRUNCATE
Answer: A. VACUUM
Explanation:
The VACUUM command in Delta Lake is used to clean up old data files that are no longer needed due to updates or deletes. These obsolete files are retained for a configurable retention period (default is 7 days) to support time travel. Running VACUUM helps maintain performance and storage efficiency by permanently deleting these unused files from disk.
4. Which Delta Lake feature allows querying previous versions of a table?
A. Delta Schema
B. Delta Audit
C. Delta Time Travel
D. Delta Restore
Answer: C. Delta Time Travel
Explanation:
Delta Time Travel allows users to access and query older snapshots of a Delta table by using either a version number or a timestamp. This is useful for debugging, data audits, or restoring previous versions of data. Time Travel is possible because Delta retains old versions of the data for a defined retention period, enabling reproducibility and traceability.
5. Which of the following best describes the merge operation in Delta Lake?
A. It replaces the table with new data.
B. It merges two tables into one without duplicates.
C. It conditionally updates, inserts, or deletes data.
D. It combines schemas from multiple sources.
Answer: C. It conditionally updates, inserts, or deletes data.
Explanation:
The MERGE operation (also known as upsert) in Delta Lake enables complex operations like conditional updates, inserts, or deletes in a single statement. This is particularly useful for handling slowly changing dimensions in data warehouses. It matches records based on a condition and applies transformations accordingly, ensuring data consistency and reducing pipeline complexity.
6. What is the default file format for Spark DataFrame write operations if no format is specified?
A. Delta
B. CSV
C. Parquet
D. Avro
Answer: C. Parquet
Explanation:
Apache Spark defaults to the Parquet format when writing DataFrames, unless a different format is specified. Parquet is a columnar storage format that is efficient for analytical queries due to its compression and encoding features. While Databricks supports multiple formats like JSON, Avro, and Delta, Parquet remains the standard default for Spark write operations due to its performance advantages in big data workloads.
7. In Databricks, which of the following formats supports schema evolution?
A. CSV
B. JSON
C. Parquet
D. Delta
Answer: D. Delta
Explanation:
Delta Lake supports schema evolution, allowing the schema of a table to be modified as new data arrives. This feature ensures that when new columns are introduced, Delta can automatically handle changes without manual intervention. In contrast, formats like CSV or Parquet require schema management to be handled externally, making Delta more suitable for dynamic datasets.
8. Which of the following is true about Databricks Auto Loader?
A. It supports only CSV file formats.
B. It cannot handle schema inference.
C. It incrementally processes new files as they arrive.
D. It requires manual tracking of ingested files.
Answer: C. It incrementally processes new files as they arrive.
Explanation:
Auto Loader is a Databricks utility that allows for incremental and efficient ingestion of new files from cloud storage. It uses a combination of file notification services or directory listings to detect and ingest only new data, reducing the need for full scans. It supports schema inference and evolution and is optimized for scalability in streaming data pipelines.
9. What is a key benefit of using Delta Live Tables (DLT)?
A. It replaces SQL syntax with Java code.
B. It eliminates the need for version control.
C. It provides built-in data quality and pipeline monitoring.
D. It only supports batch processing.
Answer: C. It provides built-in data quality and pipeline monitoring.
Explanation:
Delta Live Tables (DLT) is a framework for building reliable ETL pipelines in Databricks. One of its core benefits is built-in support for monitoring, lineage tracking, and enforcing data quality rules using expectations. DLT simplifies the development and operation of production-grade pipelines with automatic retries, orchestration, and reporting on data freshness and quality metrics.
10. Which of the following commands is used to create a managed Delta table in SQL?
A. CREATE DELTA TABLE ...
B. CREATE TABLE ... USING DELTA
C. CREATE MANAGED TABLE ... DELTA
D. CREATE DELTA MANAGED TABLE
Answer: B. CREATE TABLE ... USING DELTA
Explanation:
To create a managed Delta table using SQL in Databricks, the correct syntax is CREATE TABLE table_name USING DELTA. This instructs the system to store the data in Delta Lake format and manage the table’s metadata and storage location within the metastore. Managed tables are handled entirely by Databricks, simplifying lifecycle management.
11. What does the .option("mergeSchema", "true") setting do in Delta Lake?
A. Forces overwrite of existing schema
B. Merges new schema fields with existing ones
C. Prevents schema changes
D. Drops columns not present in new data
Answer: B. Merges new schema fields with existing ones
Explanation:
The .option("mergeSchema", "true") flag enables schema evolution during writes to a Delta table. If the incoming data includes new columns not present in the table schema, those columns will be added to the schema instead of throwing an error. This is especially useful in evolving datasets where columns may be added over time.
12. In Spark, what is the purpose of the .repartition(n) method?
A. Increases memory usage
B. Sorts the data
C. Reduces the number of tasks
D. Changes the number of partitions
Answer: D. Changes the number of partitions
Explanation:
The .repartition(n) method in Spark is used to increase or decrease the number of partitions in a DataFrame. This is crucial for optimizing parallelism and managing resource utilization during distributed processing. Increasing partitions can improve parallel processing for large datasets, while reducing them can improve performance when fewer tasks are sufficient.
13. What is the main difference between a managed and an external table in Databricks?
A. Managed tables support more file formats
B. External tables cannot be queried
C. Managed tables store data in the metastore location
D. External tables do not support Delta format
Answer: C. Managed tables store data in the metastore location
Explanation:
In Databricks, managed tables are stored in the default metastore location, and their data is managed by Databricks. If a managed table is dropped, its data is deleted automatically. External tables, on the other hand, reference data stored outside of the metastore (e.g., cloud storage) and remain even if the table is dropped, offering more flexibility in data control.
14. Which API is used to perform streaming reads in Structured Streaming with Auto Loader?
A. .read()
B. .read.format("delta")
C. .readStream.format("cloudFiles")
D. .readStream()
Answer: C. .readStream.format("cloudFiles")
Explanation:
When using Auto Loader with Structured Streaming in Databricks, the correct API is .readStream.format("cloudFiles"). This format allows incremental file ingestion from cloud storage, supporting schema inference, file tracking, and scalable streaming ingestion. It is highly optimized for continuously arriving data.
15. What does the .trigger(once=True) option do in Structured Streaming?
A. Disables the streaming job
B. Runs one batch and stops
C. Continuously triggers every second
D. Keeps running until manually stopped
Answer: B. Runs one batch and stops
Explanation:
In Structured Streaming, the .trigger(once=True) option allows the streaming query to process all available data once and then stop. This is useful when you want the advantages of streaming (e.g., schema inference, incremental processing) in a batch-like context. It avoids the overhead of continuous processing and is commonly used in ETL workflows.
16. What does Delta Lake use to track the versions of a table?
A. JSON log files
B. Versioned CSVs
C. Transaction log (_delta_log)
D. Hive Metastore
Answer: C. Transaction log (_delta_log)
Explanation:
Delta Lake maintains a transaction log directory named _delta_log inside each Delta table’s directory. This log records every operation performed on the table in JSON and Parquet formats, enabling features like ACID transactions, time travel, and schema evolution. It allows Delta to track and reconstruct previous versions of the table, making it a foundational component of Delta architecture.
17. Which of the following commands can show the history of a Delta table?
A. SHOW VERSIONS
B. DESCRIBE HISTORY table_name
C. SHOW LOGS FOR table_name
D. LIST DELTA VERSIONS
Answer: B. DESCRIBE HISTORY table_name
Explanation:
The SQL command DESCRIBE HISTORY table_name returns the version history of a Delta table, including details such as operation type (MERGE, WRITE), timestamp, user, and operation metrics. This is useful for audit trails, debugging data changes, or identifying schema modifications over time. It leverages the Delta transaction log.
18. What happens when you overwrite a Delta table with the overwriteSchema option set to true?
A. The table is dropped and recreated.
B. The table schema is updated to match the new DataFrame.
C. The table is deleted.
D. It appends the data with new columns.
Answer: B. The table schema is updated to match the new DataFrame.
Explanation:
Using .option("overwriteSchema", "true") while writing to a Delta table enables schema replacement. This updates the table’s existing schema to match that of the incoming DataFrame, including added, removed, or changed columns. This operation is often used during schema evolution or restructuring pipelines but should be used cautiously to avoid unintended data loss.
19. What is the purpose of Z-Ordering in Delta Lake?
A. Encrypting Delta tables
B. Sorting data for better compression
C. Optimizing read performance on selective queries
D. Tracking schema versions
Answer: C. Optimizing read performance on selective queries
Explanation:
Z-Ordering is a technique used in Delta Lake to colocate related information in the same set of files. This optimization reorganizes data on disk based on specified columns, improving data skipping and read performance during queries with filters. For example, filtering on a customer_id or date column is significantly faster after Z-Ordering.
20. Which of the following statements is true about the OPTIMIZE command in Delta Lake?
A. It rewrites metadata only
B. It compresses all columns using GZIP
C. It compacts small files into larger ones
D. It deletes null records
Answer: C. It compacts small files into larger ones
Explanation:
The OPTIMIZE command in Delta Lake combines small data files into larger, more efficient files. This reduces file fragmentation and improves query performance by minimizing the number of files Spark must scan. It’s particularly useful in scenarios where frequent writes create many small files, such as streaming or micro-batch ingestion.
21. What is one use of EXPLAIN in Spark SQL?
A. Encrypt query results
B. Cancel a running query
C. View the logical and physical plan
D. Modify the query schema
Answer: C. View the logical and physical plan
Explanation:
The EXPLAIN command in Spark SQL displays the logical and physical execution plan of a query. This helps developers and engineers understand how Spark will execute the query, optimize joins, partitions, and read paths. It is a valuable tool for debugging and performance tuning Spark SQL queries.
22. How does Auto Loader track files that have already been processed?
A. File names
B. File timestamps
C. Checkpoint and metadata logs
D. File sizes
Answer: C. Checkpoint and metadata logs
Explanation:
Auto Loader uses checkpointing and metadata logs to keep track of which files have been processed. This ensures exactly-once ingestion and avoids duplicate processing. By leveraging either file notification services or directory listings, Auto Loader guarantees reliability and scalability even when processing millions of files in streaming pipelines.
23. What is the main purpose of using a checkpoint location in Spark Structured Streaming?
A. Improve write speed
B. Track data quality
C. Maintain state and track progress
D. Create temporary backups
Answer: C. Maintain state and track progress
Explanation:
A checkpoint location in Spark Structured Streaming is used to persist the streaming query’s state, progress, and metadata. It ensures fault tolerance by allowing a streaming query to resume from where it left off in case of failure or restart. Without checkpointing, Spark would reprocess all data from the source.
24. In Databricks SQL, which file format is required for using the COPY INTO command?
A. Delta
B. JSON
C. CSV
D. Any supported file format
Answer: D. Any supported file format
Explanation:
The COPY INTO command in Databricks SQL can ingest data from any supported file format, including CSV, JSON, Parquet, and Avro. It is used to load data into Delta tables and supports schema inference, data skipping, and conditional loading. The flexibility in file formats makes it ideal for various ETL scenarios.
25. What does .foreachBatch() do in Structured Streaming?
A. Writes data to Delta Lake once
B. Triggers batch jobs manually
C. Processes each micro-batch using custom logic
D. Skips writing micro-batches
Answer: C. Processes each micro-batch using custom logic
Explanation:
The .foreachBatch() method allows developers to apply custom operations to each micro-batch of data in Structured Streaming. This is useful when more control is needed than standard write methods provide—such as writing to external systems, performing upserts, or invoking APIs. Each micro-batch is treated as a standard DataFrame.
26. What is one benefit of Delta Lake’s ACID compliance?
A. Schema standardization
B. Improved data compression
C. Reliable concurrent read and write operations
D. Lower cloud storage costs
Answer: C. Reliable concurrent read and write operations
Explanation:
Delta Lake’s ACID (Atomicity, Consistency, Isolation, Durability) compliance ensures reliable data operations even in concurrent environments. Multiple users or jobs can read and write to a Delta table simultaneously without corrupting data or causing inconsistencies. This makes it suitable for enterprise-grade pipelines and data lakes that require high reliability.
27. What is the function of spark.sql.shuffle.partitions in a Databricks notebook?
A. Sets memory allocation
B. Controls number of partitions during shuffles
C. Tracks query progress
D. Limits file size
Answer: B. Controls number of partitions during shuffles
Explanation:
The spark.sql.shuffle.partitions setting determines the number of partitions created during shuffle operations, such as joins and aggregations. Tuning this value is essential for performance optimization. A value too high leads to excessive small tasks; too low results in large, inefficient tasks. The default is 200, but it can be adjusted for workload characteristics.
28. What happens if a Delta table is queried while it is being updated?
A. Query fails
B. Query waits until update is complete
C. Query reads a consistent snapshot
D. Query reads partial updated data
Answer: C. Query reads a consistent snapshot
Explanation:
Delta Lake provides snapshot isolation, meaning that a query reads a consistent version of the table at the start of execution—even if the table is being updated concurrently. This ensures read consistency and avoids issues like dirty reads, making Delta reliable for both streaming and batch workloads.
29. How can you ensure idempotent writes to a Delta table from a streaming job?
A. Disable checkpoints
B. Use append mode
C. Use .outputMode("complete")
D. Enable checkpointing and deduplication logic
Answer: D. Enable checkpointing and deduplication logic
Explanation:
To ensure idempotent writes in Structured Streaming, checkpointing must be enabled so that Spark remembers what it has already processed. Additionally, deduplication logic (like using unique keys or watermarking) can prevent duplicate entries in the Delta table. These practices together ensure exactly-once semantics for reliable data processing.
30. Which tool provides a visual interface to monitor and manage data pipelines in Databricks?
A. Unity Catalog
B. Auto Loader
C. Data Explorer
D. Delta Live Tables (DLT) UI
Answer: D. Delta Live Tables (DLT) UI
Explanation:
Delta Live Tables (DLT) includes a dedicated UI within Databricks that enables users to visually track pipeline status, data quality metrics, lineage, and operational logs. It simplifies monitoring, debugging, and managing ETL pipelines, making it easier to ensure data freshness, resolve issues, and ensure SLA compliance in production.
