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DCDEP exam Format | DCDEP Course Contents | DCDEP Course Outline | DCDEP exam Syllabus | DCDEP exam Objectives

Exam Code: DCDEP

Exam Name: Databricks Certified Data Engineer Professional

Type: Proctored certification

Total number of questions: 60

Time limit: 120 minutes

Question types: Multiple choice



Section 1: Databricks Tooling

- Explain how Delta Lake uses the transaction log and cloud object storage to certain atomicity and durability

- Describe how Delta Lakes Optimistic Concurrency Control provides isolation, and which transactions might conflict

- Describe basic functionality of Delta clone.

- Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes

- Implement Delta tables optimized for Databricks SQL service

- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)



Section 2: Data Processing (Batch processing, Incremental processing, and Optimization)

- Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance

- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)

- Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files.

- Articulate multiple strategies for updating 1+ records in a spark table (Type 1)

- Implement common design patterns unlocked by Structured Streaming and Delta Lake.

- Explore and tune state information using stream-static joins and Delta Lake

- Implement stream-static joins

- Implement necessary logic for deduplication using Spark Structured Streaming

- Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read

- Leverage CDF to easily propagate deletes

- Demonstrate how proper partitioning of data allows for simple archiving or deletion of data

- Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries



Section 3: Data Modeling

- Describe the objective of data transformations during promotion from bronze to silver

- Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture

- Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables.

- Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads.

- Implement best practices when streaming data from multiplex bronze tables.

- Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver

- Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake

- Implement tables avoiding issues caused by lack of foreign key constraints

- Add constraints to Delta Lake tables to prevent bad data from being written

- Implement lookup tables and describe the trade-offs for normalized data models

- Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads.

- Implement SCD Type 0, 1, and 2 tables



Section 4: Security & Governance

- Create Dynamic views to perform data masking

- Use dynamic views to control access to rows and columns



Section 5: Monitoring & Logging

- Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications.

- Inspect event timelines and metrics for stages and jobs performed on a cluster

- Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications.

- Design systems that control for cost and latency SLAs for production streaming jobs.

- Deploy and monitor streaming and batch jobs



Section 6: Testing & Deployment

- Adapt a notebook dependency pattern to use Python file dependencies

- Adapt Python code maintained as Wheels to direct imports using relative paths

- Repair and rerun failed jobs

- Create Jobs based on common use cases and patterns

- Create a multi-task job with multiple dependencies

- Design systems that control for cost and latency SLAs for production streaming jobs.

- Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters.

- Execute commands from the CLI to deploy and monitor Databricks jobs.

- Use REST API to clone a job, trigger a run, and export the run output

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