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

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

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최신 Databricks Certification Certified-Data-Engineer-Professional 무료샘플문제 (Q123-Q128):

질문 # 123
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
B. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
C. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
D. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

정답：D

설명：
The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.

질문 # 124
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

정답：C

설명：
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

질문 # 125
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
B. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
D. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.

정답：B

질문 # 126
When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A. In the Query Profiler, under Query Source
B. In the Spart UI, under the Jobs tab
C. In the Query Profiler, under the Stages tab
D. In the Spark UI, under the SQL/DataFrame tab

정답：D

설명：
The Spark UI provides detailed visibility into how queries are executed. The SQL/DataFrame tab displays the logical and physical query plans, allowing engineers to visualize execution details and understand how the workload is processed across stages and operators.

질문 # 127
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Run
B. Can Read
C. No permissions
D. Can Edit
E. Can Manage

정답：B

질문 # 128
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