Data Engineering on Microsoft Azure
DP-203
Money Back Guarantee
98% Success Rate
Real Questions
1
Design and implement data storage 20%
1.1
Implement a partition strategy
5 subtopics1.1.1Implement a partition strategy for files
1.1.2Implement a partition strategy for analytical workloads
1.1.3Implement a partition strategy for streaming workloads
1.1.4Implement a partition strategy for Azure Synapse Analytics
1.1.5Identify when partitioning is needed in Azure Data Lake Storage Gen2
Learning Objectives
- Design and implement appropriate partition strategies for different workload types
- Optimize query performance through effective partitioning
- Identify scenarios where partitioning provides value
1.2
Design and implement the data exploration layer
4 subtopics1.2.1Create and execute queries using SQL serverless and Spark clusters
1.2.2Recommend and implement Azure Synapse Analytics database templates
1.2.3Push new or updated data lineage to Microsoft Purview
1.2.4Browse and search metadata in Microsoft Purview Data Catalog
Learning Objectives
- Design efficient data exploration architectures
- Implement appropriate compute solutions for different scenarios
- Establish data governance and cataloging practices
Domain Hands-on Skills
Creating partitioned tables in SynapseImplementing file partitioning strategiesConfiguring Microsoft Purview integrationWriting optimized queries for partitioned data
Common Mistakes to Avoid
- Over-partitioning leading to small files
- Incorrect distribution key selection
- Not considering data skew
- Ignoring partition elimination in queries
2
Develop data processing 45%
3