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

Certified-Data-Engineer-Professional actual test

About Databricks Certified Data Engineer Professional : Certified-Data-Engineer-Professional Exam

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

Section	Objectives
Data Modelling	- Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Design and implement scalable data models using Delta Lake 3. Understand Liquid Clustering versus partitioning and Z-Ordering - Dimensional Modelling 1. Design dimensional models for analytical workloads
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 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
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
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. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering
Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 2. Develop User-Defined Functions using Pandas/Python UDFs 3. Manage and troubleshoot third-party library installations and dependencies - Building and Testing ETL Pipelines 1. Use control flow operators in pipeline components 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Use APPLY CHANGES APIs for change data capture 4. Configure environments, dependencies, memory, and retry behavior 5. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 6. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 7. Develop unit and integration tests for data processing code 8. Compare streaming tables and materialized views
Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Share live Lakehouse data with external computing platforms 3. Configure sharing with external platforms using the open sharing protocol
Ensuring Data Security and Compliance	- Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information - 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
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. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles
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 Query Profiler and Spark UI to monitor workloads 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use system tables for resource, cost, audit, and workload monitoring 4. Use Databricks REST APIs and CLI for monitoring jobs and pipelines

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. The source data is valid JSON but does not conform to the defined schema in some way.
B. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
C. At some point, the upstream data provider switched everything to multi-line JSON.
D. The badRecordsPath location is accumulating many small files.

Reveal Solution Discussion 0

Correct Answer: A 🗳️