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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionObjectives
Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
  • 1. Manage and troubleshoot third-party library installations and dependencies
    • 2. Develop User-Defined Functions using Pandas/Python UDFs
      • 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
        - Building and Testing ETL Pipelines
        • 1. Configure environments, dependencies, memory, and retry behavior
          • 2. Use APPLY CHANGES APIs for change data capture
            • 3. Develop unit and integration tests for data processing code
              • 4. Compare streaming tables and materialized views
                • 5. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                  • 6. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                    • 7. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                      • 8. Use control flow operators in pipeline components
                        Debugging and Deploying- Debugging and Troubleshooting
                        • 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                          • 2. Analyze errors and remediate failed job runs
                            • 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                              - Deploying CI/CD
                              • 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                  Data Governance- Unity Catalog Permissions
                                  • 1. Understand the Unity Catalog permission inheritance model
                                    - Metadata and Discoverability
                                    • 1. Create and maintain descriptions and metadata for enterprise data
                                      Cost & Performance Optimisation- Query Performance
                                      • 1. Identify inefficient joins and excessive data shuffling
                                        • 2. Use Query Profile to identify performance bottlenecks
                                          - Delta Optimization
                                          • 1. Understand deletion vectors and liquid clustering
                                            • 2. Apply data skipping and file pruning techniques
                                              • 3. Use Change Data Feed to address streaming table limitations and improve latency
                                                - Cost Optimization
                                                • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                  Monitoring and Alerting- Monitoring
                                                  • 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                    • 2. Use Query Profiler and Spark UI to monitor workloads
                                                      • 3. Use system tables for resource, cost, audit, and workload monitoring
                                                        • 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                          - Alerting
                                                          • 1. Use SQL Alerts for data quality monitoring
                                                            • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                              Ensuring Data Security and Compliance- Data Security
                                                              • 1. Use row filters and column masks for sensitive data
                                                                • 2. Apply anonymization and pseudonymization techniques
                                                                  • 3. Use ACLs to secure workspace objects and enforce least privilege
                                                                    - Compliance
                                                                    • 1. Develop data purging solutions according to data retention policies
                                                                      • 2. Implement pipelines that detect and mask personally identifiable information
                                                                        Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                                                        • 1. Build append-only pipelines for batch and streaming data using Delta
                                                                          • 2. Ingest data from message buses and cloud storage
                                                                            • 3. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                                                              Data Transformation, Cleansing, and Quality- Advanced Data Transformation
                                                                              • 1. Write efficient Spark SQL and PySpark transformations
                                                                                • 2. Apply window functions, joins, and aggregations to large datasets
                                                                                  - Data Quality
                                                                                  • 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                                                                                    • 2. Develop data quarantining processes for invalid data
                                                                                      Data Sharing and Federation- Delta Sharing
                                                                                      • 1. Configure sharing with external platforms using the open sharing protocol
                                                                                        • 2. Configure Databricks-to-Databricks Sharing
                                                                                          • 3. Share live Lakehouse data with external computing platforms
                                                                                            - Lakehouse Federation
                                                                                            • 1. Configure Lakehouse Federation with appropriate governance
                                                                                              Data Modelling- Dimensional Modelling
                                                                                              • 1. Design dimensional models for analytical workloads
                                                                                                - Scalable Data Models
                                                                                                • 1. Understand Liquid Clustering versus partitioning and Z-Ordering
                                                                                                  • 2. Optimize data layout using Liquid Clustering
                                                                                                    • 3. Design and implement scalable data models using Delta Lake

                                                                                                      Databricks Certified-Data-Engineer-Professional Exam — Answers to Your Questions

                                                                                                      The Databricks Certified Data Engineer Professional (exam code Certified-Data-Engineer-Professional) is the exam you pass to earn the Databricks Certification certification, a credential at the Professional level. It is also connected with related credentials such as Databricks Certified Data Engineer Associate. If you are mapping out a certification path with Databricks, this exam is a milestone worth planning around.

                                                                                                      The Certified-Data-Engineer-Professional exam includes 59 scored questions questions, and you have 120 minutes to complete them. With that many questions on the clock, pacing is part of the challenge: divide your time into rough blocks as you go, and flag the items that stall you so you can circle back at the end instead of burning minutes on a single question. A week or two before your exam date, sit at least one full timed session in the TestKingsIT test engine under the same time limit, so the pace feels familiar rather than frantic on the day.

                                                                                                      You need Not publicly specified in the current official exam guide to pass the Certified-Data-Engineer-Professional exam, and the official registration fee is USD 200, plus applicable taxes as required by local law. That fee is due again in full for every retake, which makes thorough preparation the cheaper investment by far. Before you book your seat, test yourself with the TestKingsIT practice questions until you are scoring comfortably above the passing mark on a consistent basis — it is the simplest way to avoid paying for the same exam twice.

                                                                                                      No mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam.

                                                                                                      Eligibility rules can be adjusted by Databricks over time, so before you register, confirm the current requirements on the official exam page: view the official Certified-Data-Engineer-Professional exam information.

                                                                                                      Yes. TestKingsIT offers a free PDF demo of the Certified-Data-Engineer-Professional practice questions, so you can judge the quality of the material firsthand before paying anything. Every purchase also includes 365 days of free updates — if Databricks changes the exam during that year, your material changes with it. And when the free update period expires, you can extend it at a 50% discount from within your member zone.

                                                                                                      Every Certified-Data-Engineer-Professional purchase at TestKingsIT is backed by a 100% Money Back Guarantee: if you take the corresponding exam within 60 days of your purchase and do not pass, you can apply for a full refund. The guarantee has clear conditions — it does not apply if you sit the exam within 3 days of purchase, if you downloaded the material but never actually took the exam, or to free materials and expired orders, and the candidate name must match the payer name. To file a claim, send a scan of your exam enrollment slip together with your official Score Report PDF within 2 days of the exam; claims are processed within 7 days. If you would rather not take a refund, you can exchange the product instead and receive two additional exam preparation products of equal value for free, while your original product keeps its update service. Delivery itself is instant: your material is available for download right after payment and is also sent to your email within one minute — if it has not arrived within 2 hours, contact our customer service. There is no limit on how many computers you can install it on.

                                                                                                      The official Databricks Certified Data Engineer Professional outline divides the exam content into 10 main domains. The first three are:

                                                                                                      • Monitoring and Alerting
                                                                                                      • Developing Code for Data Processing using Python and SQL
                                                                                                      • Data Governance

                                                                                                      That is only the headline view — scroll back up to the Exam Topics section on this page for the complete domain-by-domain breakdown before you plan your study schedule.

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
                                                                                                      If task A fails during a scheduled run, which statement describes the results of this run?

                                                                                                      • A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
                                                                                                      • B. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
                                                                                                      • C. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
                                                                                                      • D. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
                                                                                                      • E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for TestKingsIT members. You can sign-up / login (it's free).

                                                                                                      A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

                                                                                                      • A. Share tables WITHOUT HISTORY and enable partitioning for better query performance.
                                                                                                      • B. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
                                                                                                      • C. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
                                                                                                      • D. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for TestKingsIT members. You can sign-up / login (it's free).

                                                                                                      The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
                                                                                                      Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

                                                                                                      • A. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
                                                                                                      • B. Schedule a job to execute the pipeline once an hour on a new job cluster.
                                                                                                      • C. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
                                                                                                      • D. Configure a job that executes every time new data lands in a given directory.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for TestKingsIT members. You can sign-up / login (it's free).

                                                                                                      A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

                                                                                                      • A. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
                                                                                                      • B. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
                                                                                                      • C. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
                                                                                                      • D. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: D  🗳️

                                                                                                      Explanation: Only visible for TestKingsIT members. You can sign-up / login (it's free).

                                                                                                      The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
                                                                                                      item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
                                                                                                      The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
                                                                                                      A junior data engineer suggests converting this data to Delta Lake will improve query performance.
                                                                                                      Which response to the junior data engineer's suggestion is correct?

                                                                                                      • A. Delta Lake statistics are only collected on the first 4 columns in a table.
                                                                                                      • B. Delta Lake statistics are not optimized for free text fields with high cardinality.
                                                                                                      • C. The Delta log creates a term matrix for free text fields to support selective filtering.
                                                                                                      • D. Text data cannot be stored with Delta Lake.
                                                                                                      • E. ZORDER ON review will need to be run to see performance gains.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for TestKingsIT members. You can sign-up / login (it's free).

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