Databricks Certified Data Engineer Professional Dumps & Exam Prep Guide

Prepare for the Databricks Certified Data Engineer Professional exam with these latest dumps and practice questions. Tailored for advanced data engineers, this resource covers in-depth topics like advanced Spark optimization, structured streaming, Delta Live Tables (DLT), Databricks SQL, and MLOps on the Lakehouse platform. Each dump and practice test reflects the exam's real difficulty level, helping you identify knowledge gaps and boost confidence. Whether you're looking for free Databricks professional dumps or simulated test scenarios, these materials provide comprehensive, hands-on readiness for your certification. Ideal for seasoned data professionals aiming to validate their elite skills with Databricks technologies.

Databricks Certified Data Engineer Professional Dumps & Sample Questions List :

1. You need to create a deep clone of a Delta table that is currently stored on an external storage location. Which of the following conditions must be met for the deep clone operation to succeed?

1. The deep clone process requires you to manually copy data files before executing the clone operation.

2. The deep clone operation does not require any additional permissions beyond metadata access.

3. The storage account must allow read and write permissions for the source and target locations.

4. The source table must not have any active readers.

2. You are tasked with writing a large PySpark DataFrame to disk in parquet format, but you need to manually control the size of the part-files to optimize the read performance in a downstream ETL process. Which combination of actions should you take to control the size of the individual part-files when saving the DataFrame? (Select two)

1. Configure the spark.sql.files.maxPartitionBytes to set the maximum file size for part-files generated.

2. Use the coalesce(n) method before writing the DataFrame, where n is the desired number of output files.

3. Use the repartition(n) method before writing the DataFrame, where n is based on the size of the part-files you want to generate.

4. Manually calculate the DataFrame size and write the DataFrame using a custom file writer to manage file size.

5. Enable the spark.sql.files.maxRecordsPerFile configuration, setting it to limit the number of records per part-file.

3. You are implementing a streaming pipeline in Databricks to ingest log data from IoT devices into the bronze layer of your Delta Lake. The data arrives continuously with some malformed records, missing fields, and out-of-range values. You need to promote the data to the silver layer to ensure that it can be used in real-time monitoring dashboards. Which transformation step is the most critical when promoting the streaming IoT data from the bronze layer to the silver layer in this scenario?

1. Time Travel Querying: Implementing time travel features to track changes to the dataset over time and query the dataset as it existed at any specific point.

2. Schema Enforcement: Enforcing strict schema validation rules to reject any data that does not conform to the expected structure or data types, while preserving valid data.

3. Outlier Detection: Identifying and removing data points that fall outside of the expected range for sensor readings in the IoT data.

4. Upsert (MERGE INTO): Merging incoming streaming records into an existing dataset in the silver layer based on a unique device ID.

4. You are designing a production streaming system that processes real-time financial transactions. The system must meet stringent cost and latency SLAs, with sub-second latency requirements and a maximum cloud infrastructure budget. Which of the following techniques would be most effective for optimizing the system to meet both cost and latency SLAs?

1. Apply Trigger.Once to minimize cluster resource usage by processing batches only when new data arrives.

2. Reduce the number of shuffle operations by optimizing the data partitioning to minimize network overhead during processing.

3. Use a large cluster size with many small executors to reduce task overhead and achieve lower latency.

4. Enable high checkpoint frequency to reduce the risk of data loss, even if it leads to increased I/O operations.

5. Leverage auto-scaling for the cluster, adjusting the number of nodes based on workload demand to balance cost and performance.

5. You have a Databricks notebook that performs real-time streaming ETL using Structured Streaming and Delta Lake. Recently, there have been intermittent failures, and the job is automatically retrying but is still failing after a few attempts. To monitor and troubleshoot these failures, which logging technique would best capture detailed error information about what went wrong?

1. Add cloud-native logging (e.g., AWS CloudWatch, Azure Monitor) to log all Databricks errors across the cluster.

2. Turn on Spark Event Logs to capture detailed information about the transformations and actions in the job.

3. Use the Delta Lake Logs to capture streaming-specific logs and checkpoints related to job execution.

4. Enable Audit Logs to track who ran the job and what operations were performed.

5. Enable Structured Streaming Progress Logs to capture the state of the streaming queries and any errors during each micro-batch.

6. You are designing a customer dimension table to track customer information such as name, email, and address. The business requires that only the most recent information for each customer be retained in the table, with no history of previous changes. You need to implement this as a Slowly Changing Dimension (SCD) Type 1 table in Delta Lake. Which of the following is the correct approach to implement this in Delta Lake?

1. Use Delta Lake's MERGE INTO operation to overwrite existing records with new data for each customer.

2. Use Delta Lake's UPDATE statement to modify only specific fields that have changed, leaving other fields untouched.

3. Partition the Delta Lake table by the customer ID and apply UPSERT operations to each partition, retaining historical data.

4. Implement a Delta Lake table with a versioned column to track changes but only expose the latest version of each record.

7. You are tasked with creating a cloned version of a Delta Lake table to test modifications on data without affecting the source table. Given the following table structure:

• The source Delta table stores transactional data with millions of records and daily updates.

• You need to create a clone for experimenting with schema changes and validate transformations.

Which of the following actions should be considered when choosing between a shallow or deep clone? (Select two)

1. Shallow clone creates a full copy of the data, which can significantly increase storage usage.

2. Shallow clone creates a reference to the source table's data files and metadata without copying the actual data.

3. Deep clone is more efficient for quickly experimenting with schema changes since it avoids copying the data files.

4. Deep clone copies both data and metadata from the source table, creating a completely independent copy of the table.

5. Changes to the shallow clone are reflected back in the source table, which can disrupt production data integrity.

8. A data engineer is working with a Databricks notebook and needs to install a Python package from PyPI. They want to ensure that the package is installed on all worker nodes in the cluster, but only for the duration of their notebook session. Which of the following methods would achieve this?

1. Add the package to the cluster libraries in the Databricks UI

2. Use %conda install in a notebook cell

3. Use dbutils.library.install in a notebook cell to install the package

4. Use %sh pip install in a notebook cell

5. Use %pip install in a notebook cell

9. A data engineer is tasked with ensuring that all Delta Lake tables are created as external, unmanaged tables in a Lakehouse environment. What is the correct approach to guarantee that a table is external and unmanaged?

1. Specify the DELTA_TABLE_TYPE as UNMANAGED in the Delta Lake configuration.

2. Set the AUTO_MANAGE flag to OFF in the workspace settings.

3. Use the LOCATION keyword when creating the table to specify the external storage path.

4. Set the EXTERNAL_TABLE parameter to TRUE in the table creation statement.

5. Add a CLEANUP_POLICY to disable automatic management for Delta tables.

10. You are working with a Delta Lake table that tracks product inventory. Due to frequent updates and deletions in the dataset, you decide to use Change Data Feed (CDF) to simplify downstream consumption of these changes by other systems. What is the primary advantage of using CDF in this scenario compared to traditional methods for tracking and propagating changes?

1. CDF enables users to partition tables automatically based on changed data to optimize incremental loads

2. CDF automatically propagates all changes to external systems without requiring manual intervention

3. CDF creates new versions of the entire dataset, which optimizes query performance for read-heavy operations

4. CDF provides an efficient way to identify only the rows that have been inserted, updated, or deleted since the last time data was read.

11. You are working in a shared Delta Lake environment, where multiple users are running concurrent jobs to read and update a large Delta table. Which of the following scenarios could lead to a conflict when using Delta Lake's Optimistic Concurrency Control? (Select two)

1. Two concurrent append operations that add new rows to the Delta table.

2. A write operation on a Delta table with a static schema and a concurrent schema evolution operation.

3. Two concurrent write operations attempt to modify the same rows in the Delta table.

4. A read operation and a concurrent write operation occur on the same table.

5. Two concurrent write operations attempt to modify different partitions of the Delta table.

12. You are tasked with designing a data model for a retail system. The system includes tables that store information about orders, products, and customers. You want to use a normalized model to reduce data redundancy and ensure data integrity. To enhance query performance, you decide to implement lookup tables for product categories and customer regions. However, some queries will involve joining these lookup tables with large fact tables. Which approach should you take to implement the lookup tables while minimizing performance issues in a normalized model?

1. Denormalize the lookup tables by embedding them into the fact tables to avoid joins during query execution.

2. Use broadcast joins with lookup tables to minimize the performance impact of joining them with large fact tables during query execution.

3. Normalize the data model by creating separate lookup tables for product categories and customer regions and use join operations in queries to maintain data integrity.

4. Partition the fact tables based on product category and customer region to optimize performance when querying against the lookup tables.

13. You are working with a large PySpark DataFrame consisting of over 100 million rows of customer transaction data. To optimize storage and future read performance, you need to write this DataFrame to disk in a highly efficient format (e.g., Parquet) while ensuring each part-file is approximately 1GB in size. Your cluster consists of 10 nodes, and you want to balance file size with the number of output files to avoid creating too many small files. Which of the following approaches will best allow you to manually control the size of the output files when writing the DataFrame to disk?

1. Use the .coalesce() method to reduce the number of partitions based on your desired file size, and then write the DataFrame to disk.

2. Use the .write() method with the maxRecordsPerFile option set to control the size of individual part-files based on the number of rows.

3. Use the .repartitionByRange() method to partition the data based on a specific column range, ensuring evenly sized part-files.

4. Use the .repartition() method to set the number of partitions to match the desired part-file count, and then write the DataFrame directly to disk.

14. A data engineering team needs to adjust permission settings on a Databricks Job after realizing that the current owner has left the organization. They need to transfer ownership to another individual in the team but are unclear about how to properly configure Databricks Jobs permissions. Which statement is accurate regarding how ownership and permissions work for Databricks Jobs?

1. Transferring ownership of a Databricks Job is only allowed between individual users, not groups or service principals.

2. Once a user creates a Databricks Job, they retain exclusive "Owner" privileges, and no other users can be assigned these privileges.

3. Groups cannot be granted any privileges for a Databricks Job, even if a workspace administrator attempts to assign permissions.

4. A Databricks Job can have multiple owners, but only workspace administrators can assign additional owners.

5. A user can transfer ownership of a Databricks Job to any other user, provided they have "Manage" or higher-level privileges.

15. A data engineer needs to install a specific Python library for data processing that is not pre-installed in the Databricks environment. They want to ensure that the library is available to all the nodes in the cluster during their session but scoped only to their notebook. What is the correct method to achieve this?

1. Use the Databricks Libraries UI to manually upload the package to the cluster.

2. Modify the cluster's init script to include the pip install command.

3. Install the package globally using !pip install in a notebook cell.

4. Use %pip install in a notebook cell to install the package on all nodes in the currently active cluster.

5. Run pip install directly in the terminal using the %sh magic command.

16. Your company’s data lakehouse is built on Delta Lake, and you are tasked with implementing a solution that allows for incremental processing of data, including propagating delete operations from the source system. You’ve decided to use Change Data Feed (CDF) to track changes, including deletes. However, you also want to ensure that delete operations do not impact queries on historical data. What is the best approach to efficiently handle and propagate these deletes while keeping the historical data intact?

1. Use CDF to identify the deleted records and delete them from the Delta table

2. Ignore CDF and run full table scans to identify and remove deleted records periodically

3. Use CDF to mark records as deleted with a custom flag, then remove them during cleanup

4. Use CDF to identify deleted records and filter them during queries, but retain them in the table

17. You are designing a data model in Databricks for a retail company that stores customer transactions. The company wants to analyze transactions on a daily and monthly basis, considering the possibility of data skew due to uneven distribution of sales in different regions. Which partitioning strategy would you choose to optimize the performance of queries that focus on date-based aggregations and why?

1. Partition by region and product_id.

2. Partition by year and month.

3. Partition by day.

4. Partition by date and region.

18. You are working with a large dataset of customer transactions stored in a Delta Lake table. The data is partitioned by the region column. You notice that during batch processing, one partition (region = 'East') has significantly more data than other partitions, causing skew in the distribution of tasks across executors. You want to optimize the distribution without increasing the number of partitions drastically. Which of the following techniques should you use?

1. repartition(4)

2. coalesce(4)

3. coalesce(1)

4. rebalance()

19. You are implementing an incremental processing pipeline for a retail company that processes customer transaction data. The data includes a transaction_id, customer_id, store_id, and transaction_date. You need to partition the data for optimal performance, ensuring that queries on recent transactions are fast and the pipeline can scale as the data grows. Which of the following partitioning strategies is the most effective for this use case?

1. Partition the data by store_id to allow queries to filter by specific stores, improving performance for store-level analysis.

2. Partition the data by transaction_date to minimize the amount of data scanned for queries that analyze recent transactions and for incremental processing.

3. Partition the data by transaction_date and customer_id to ensure optimal distribution and query performance for both time-based and customer-based queries.

4. Partition the data by transaction_id to ensure even distribution of data across partitions and to make querying individual transactions faster.

20. You have run a Spark job that performs a large-scale join operation between two datasets. The job completes, but the performance is significantly slower than expected. You navigate to the Spark UI to investigate potential bottlenecks. Which of the following sections of the Spark UI would best help you understand the stage execution time and identify skew in task distribution?

1. Storage Tab

2. SQL Tab

3. Executors Tab

4. Stages Tab

5. Jobs Tab

21. You are tasked with cloning a job in Databricks using the REST API. The job you want to clone has the ID 1234. You also need to modify the cloned job's name to Cloned Job. Which of the following REST API calls correctly clones the job and updates the name of the cloned job?

1. POST /api/2.1/jobs/clone with a request body that includes "job_id": 1234 and "new_settings": {"name": "Cloned Job"}

2. POST /api/2.1/jobs/copy with a request body that includes "job_id": 1234 and "name": "Cloned Job"

3. POST /api/2.1/jobs/create with a request body that includes "job_id": 1234 and "new_name": "Cloned Job"

4. POST /api/2.1/jobs/clone with a request body that includes "job_id": 1234 and "job_name": "Cloned Job"

22. You are a data engineer at a retail company managing a large dataset of transaction records stored in Delta Lake. The dataset is partitioned by year, month, and day. The company requires that all transaction data older than two years be archived to a secondary storage location, and data older than five years must be deleted permanently. The dataset is continuously growing, and the data is accessed both for reporting (batch queries) and for periodic audits (incremental queries). To meet these requirements, you need to design an efficient solution for archiving and deleting old data while minimizing the impact on query performance. Which of the following approaches best meets the company's requirements for archiving and deleting old data?

1. Run a simple DELETE operation on the Delta table for records older than five years, then use Delta Lake’s VACUUM to remove the files from disk.

2. Coalesce the partitions by day to reduce the total number of small files, improving the query performance for batch jobs, and then archive and delete data using the Delta Lake OPTIMIZE command.

3. Repartition the Delta table by year and month to make it easier to archive data older than two years and delete data older than five years by removing entire partitions.

4. Use Delta Lake’s Time Travel feature to query the table for transactions older than five years, archive them to a secondary location, and then run DELETE for these records followed by VACUUM to remove them permanently.

FAQs

1. What is the Databricks Certified Data Engineer Professional exam?

The Databricks Certified Data Engineer Professional exam validates your ability to build, manage, and optimize advanced data pipelines and workflows using the Databricks Lakehouse Platform.

2. How do I become a Databricks Certified Data Engineer Professional?

You need to pass the Databricks Certified Data Engineer Professional exam, which tests your expertise in ETL design, data modeling, Delta Lake optimization, and advanced SQL.

3. What are the prerequisites for the Databricks Certified Data Engineer Professional exam?

It is recommended that you hold the Databricks Certified Data Engineer Associate certification and have practical experience in data engineering and Databricks tools.

4. How much does the Databricks Certified Data Engineer Professional certification cost?

The exam costs $200 USD, though pricing may vary by region or currency.

5. How many questions are in the Databricks Certified Data Engineer Professional exam?

The exam includes 60 multiple-choice and multiple-select questions that must be completed within 120 minutes.

6. What topics are covered in the Databricks Certified Data Engineer Professional exam?

It covers Delta Lake architecture, data ingestion, transformation, optimization, job orchestration, and performance tuning.

7. How difficult is the Databricks Certified Data Engineer Professional exam?

It’s an advanced-level exam, requiring deep understanding of Databricks, Apache Spark, and complex data engineering workflows.

8. How long does it take to prepare for the Databricks Certified Data Engineer Professional exam?

Most candidates take 8–10 weeks to prepare, depending on their Databricks experience and familiarity with Spark and SQL.

9. What jobs can I get after earning the Databricks Certified Data Engineer Professional certification?

You can work as a Senior Data Engineer, Big Data Architect, ETL Engineer, or Cloud Data Specialist.

10. How much salary can I earn with a Databricks Certified Data Engineer Professional certification?

Professionals typically earn between $120,000–$160,000 per year, depending on their role, experience, and location.