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  • Exam Name: Databricks Certified Data Engineer Professional
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Certified-Data-Engineer-Professional Practice Dumps

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

SectionObjectives
Data Modelling- Dimensional Modelling
  • 1. Design dimensional models for analytical workloads
    - Scalable Data Models
    • 1. Understand Liquid Clustering versus partitioning and Z-Ordering
      • 2. Design and implement scalable data models using Delta Lake
        • 3. Optimize data layout using Liquid Clustering
          Data Governance- Metadata and Discoverability
          • 1. Create and maintain descriptions and metadata for enterprise data
            - Unity Catalog Permissions
            • 1. Understand the Unity Catalog permission inheritance model
              Data Sharing and Federation- Lakehouse Federation
              • 1. Configure Lakehouse Federation with appropriate governance
                - Delta Sharing
                • 1. Configure Databricks-to-Databricks Sharing
                  • 2. Configure sharing with external platforms using the open sharing protocol
                    • 3. Share live Lakehouse data with external computing platforms
                      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- Data Quality
                            • 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                              • 2. Develop data quarantining processes for invalid data
                                - Advanced Data Transformation
                                • 1. Write efficient Spark SQL and PySpark transformations
                                  • 2. Apply window functions, joins, and aggregations to large datasets
                                    Debugging and Deploying- Deploying CI/CD
                                    • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                      • 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                        - Debugging and Troubleshooting
                                        • 1. Analyze errors and remediate failed job runs
                                          • 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                            • 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                              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. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                                      • 2. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                                        • 3. Compare streaming tables and materialized views
                                                          • 4. Develop unit and integration tests for data processing code
                                                            • 5. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                                              • 6. Use control flow operators in pipeline components
                                                                • 7. Use APPLY CHANGES APIs for change data capture
                                                                  • 8. Configure environments, dependencies, memory, and retry behavior
                                                                    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
                                                                              Cost & Performance Optimisation- Delta Optimization
                                                                              • 1. Understand deletion vectors and liquid clustering
                                                                                • 2. Use Change Data Feed to address streaming table limitations and improve latency
                                                                                  • 3. Apply data skipping and file pruning techniques
                                                                                    - Query Performance
                                                                                    • 1. Use Query Profile to identify performance bottlenecks
                                                                                      • 2. Identify inefficient joins and excessive data shuffling
                                                                                        - Cost Optimization
                                                                                        • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                                                          Monitoring and Alerting- Alerting
                                                                                          • 1. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                                            • 2. Use SQL Alerts for data quality monitoring
                                                                                              - Monitoring
                                                                                              • 1. Use system tables for resource, cost, audit, and workload monitoring
                                                                                                • 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                                                  • 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                                                    • 4. Use Query Profiler and Spark UI to monitor workloads

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      1. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
                                                                                                      The following SQL DDL statement is executed to create a new table:

                                                                                                      Which command allows manual confirmation that these three requirements have been met?

                                                                                                      A) SHOW TABLES dev
                                                                                                      B) SHOW TBLPROPERTIES dev.pii test
                                                                                                      C) DESCRIBE DETAIL dev.pii test
                                                                                                      D) DESCRIBE HISTORY dev.pii test
                                                                                                      E) DESCRIBE EXTENDED dev.pii test


                                                                                                      2. The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
                                                                                                      The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
                                                                                                      The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
                                                                                                      Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

                                                                                                      A) Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
                                                                                                      B) Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
                                                                                                      C) Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
                                                                                                      D) Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
                                                                                                      E) Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.


                                                                                                      3. A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
                                                                                                      They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

                                                                                                      A) Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
                                                                                                      B) Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
                                                                                                      C) Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
                                                                                                      D) Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.


                                                                                                      4. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
                                                                                                      df = spark.read.format("parquet").load(f"/mnt/source/(date)")
                                                                                                      Which code block should be used to create the date Python variable used in the above code block?

                                                                                                      A) import sys
                                                                                                      date = sys.argv[1]
                                                                                                      B) dbutils.widgets.text("date", "null")
                                                                                                      date = dbutils.widgets.get("date")
                                                                                                      C) date = spark.conf.get("date")
                                                                                                      D) input_dict = input()
                                                                                                      date= input_dict["date"]
                                                                                                      E) date = dbutils.notebooks.getParam("date")


                                                                                                      5. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

                                                                                                      A) /jobs/runs/list
                                                                                                      B) /jobs/runs/get-output
                                                                                                      C) /jobs/list
                                                                                                      D) /jobs/runs/get
                                                                                                      E) /jobs/get


                                                                                                      Solutions:

                                                                                                      Question # 1
                                                                                                      Answer: E
                                                                                                      Question # 2
                                                                                                      Answer: E
                                                                                                      Question # 3
                                                                                                      Answer: A
                                                                                                      Question # 4
                                                                                                      Answer: B
                                                                                                      Question # 5
                                                                                                      Answer: E

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