Docker Certified Associate – Preparation & methodology
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Databricks
Databricks Certified Data Engineer Professional
The Databricks Certified Data Engineer Professional certification validates an individual's advanced skills in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform.
考试代码
Certified D
持续时间
120 min
Questions
60
官方先决条件
- Practical experience with Apache Spark and Databricks platform (production projects or equivalent hands-on work)
- Proficiency in SQL and Python for data processing and ETL pipelines
- Understanding of data engineering concepts: ETL/ELT, data modeling, partitioning, and performance tuning
- Familiarity with Delta Lake, Spark streaming, and cloud storage (AWS/GCP/Azure) integrations
目标职业
Data Engineer Big Data Engineer ETL Developer Analytics Engineer领域蓝图
ExamBoot模拟引擎与官方考试大纲同步。我们的自适应问题库优先考虑您快速实现目标。.
Write, optimize, and execute Python and SQL code on Databricks to process batch and streaming data using Spark APIs, Delta Lake features, and Databricks libraries.
Ingest and acquire data from diverse sources reliably into the lakehouse using Auto Loader, connectors, and ingestion best practices.
Apply transformations and cleansing, enforce schemas and constraints, and implement data quality checks and testing using Spark and Delta Lake capabilities.
Share and federate data securely across teams and external parties using Unity Catalog, Delta Sharing, and appropriate access controls.
Implement observability for pipelines and jobs through metrics, logging, monitoring, and alerting to ensure reliability and detect issues.
域名 5
Monitoring and Alerting
Implement observability for pipelines and jobs through metrics, logging, monitoring, and alerting to ensure reliability and detect issues.
域名 6
Cost & Performance Optimisation
Optimize compute, storage, and query performance and cost using cluster configuration, caching, partitioning, query tuning, and Databricks platform features.
域名 7
Ensuring Data Security and Compliance
Implement security controls, access management, encryption, and compliance practices using Unity Catalog and Databricks platform security features.
域名 8
Data Governance
Manage metadata, lineage, cataloging, and access policies to ensure proper governance and stewardship of lakehouse data assets.
域名 9
Debugging and Deploying
Debug, test, and deploy production-grade data pipelines and assets using Databricks Jobs, CLI, REST API, Asset Bundles, and CI/CD best practices.
域名 10
Data Modelling
Design and implement data models and lakehouse architectures (e.g., Medallion Architecture), including schema and modeling techniques to support analytics and downstream use.
域名 11
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学习提示
Use official exam blueprint to align study, take timed practice tests, drill weak areas, review answer rationales, and repeat timed blocks to build speed and reinforce core Databricks data engineering concepts.
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