Data Engineering on Microsoft Azure (DP-203) Practice Exam
Data Engineering on Microsoft Azure (DP-203) Practice Exam
About Data Engineering on Microsoft Azure (DP-203) Practice Exam
The Data Engineering on Microsoft Azure (DP-203) is suitable for Data Engineers to integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. This exam measures your ability to accomplish the following technical tasks: design and implement data storage; design and develop data processing; design and implement data security; and monitor and optimize data storage and data processing.
Who should take the exam?
The Data Engineering on Microsoft Azure (DP-203) exam is suitable for candidates working as Data Engineering on Microsoft Azure with solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns. A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.
Skills Acquired
- Help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques.
- Use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.
- Also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints.
- Deal with unanticipated issues swiftly, and minimize data loss.
- Design, implement, monitor, and optimize data platforms to meet the data pipelines needs.
Exam Details
- Exam Name: Data Engineering on Microsoft Azure
- Exam Code: DP-203
- Exam Price: $165 USD
- Total Questions: 40-60 Questions
- Exam Duration: 120 mins
Course Outline
The Data Engineering on Microsoft Azure (DP-203) Exam covers the latest exam updates as of May 4, 2023 -
Domain 1 - Understand to Design and implement data storage (15–20%)
1.1 Implement a partition strategy
- Learn to Implement a partition strategy for files
- Learn to Implement a partition strategy for analytical workloads
- Learn to Implement a partition strategy for streaming workloads
- Learn to Implement a partition strategy for Azure Synapse Analytics
- Learn to Identify when partitioning is needed in Azure Data Lake Storage Gen2
1.2 Design and implement the data exploration layer
- Learn to Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
- Learn to Recommend and implement Azure Synapse Analytics database templates
- Learn to Push new or updated data lineage to Microsoft Purview
- Learn to Browse and search metadata in Microsoft Purview Data Catalog
Domain 2 - Understand to Develop data processing (40–45%)
2.1 Ingest and transform data
- Learn to Design and implement incremental loads
- Learn to Transform data by using Apache Spark
- Learn to Transform data by using Transact-SQL (T-SQL)
- Learn to Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
- Learn to Transform data by using Azure Stream Analytics
- Learn to Cleanse data
- Learn to Handle duplicate data
- Learn to Handle missing data
- Learn to Handle late-arriving data
- Learn to Split data
- Learn to Shred JSON
- Learn to Encode and decode data
- Learn to Configure error handling for a transformation
- Learn to Normalize and denormalize data
- Learn to Perform data exploratory analysis
2.2 Develop a batch processing solution
- Learn to Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
- Learn to Use PolyBase to load data to a SQL pool
- Learn to Implement Azure Synapse Link and query the replicated data
- Learn to Create data pipelines
- Learn to Scale resources
- Learn to Configure the batch size
- Learn to Create tests for data pipelines
- Learn to Integrate Jupyter or Python notebooks into a data pipeline
- Learn to Upsert data
- Learn to Revert data to a previous state
- Learn to Configure exception handling
- Learn to Configure batch retention
- Learn to Read from and write to a delta lake
2.3 Develop a stream processing solution
- Learn to Create a stream processing solution by using Stream Analytics and Azure Event Hubs
- Learn to Process data by using Spark structured streaming
- Learn to Create windowed aggregates
- Learn to Handle schema drift
- Learn to Process time series data
- Learn to Process data across partitions
- Learn to Process within one partition
- Learn to Configure checkpoints and watermarking during processing
- Learn to Scale resources
- Learn to Create tests for data pipelines
- Learn to Optimize pipelines for analytical or transactional purposes
- Learn to Handle interruptions
- Learn to Configure exception handling
- Learn to Upsert data
- Learn to Replay archived stream data
2.4 Manage batches and pipelines
- Learn to Trigger batches
- Learn to Handle failed batch loads
- Learn to Validate batch loads
- Learn to Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
- Learn to Schedule data pipelines in Data Factory or Azure Synapse Pipelines
- Learn to Implement version control for pipeline artifacts
- Learn to Manage Spark jobs in a pipeline
Domain 3 - Understand to Secure, monitor, and optimize data storage and data processing (30–35%)
3.1 Implement data security
- Learn to Implement data masking
- Learn to Encrypt data at rest and in motion
- Learn to Implement row-level and column-level security
- Learn to Implement Azure role-based access control (RBAC)
- Learn to Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
- Learn to Implement a data retention policy
- Learn to Implement secure endpoints (private and public)
- Learn to Implement resource tokens in Azure Databricks
- Learn to Load a DataFrame with sensitive information
- Learn to Write encrypted data to tables or Parquet files
- Learn to Manage sensitive information
3.2 Monitor data storage and data processing
- Learn to Implement logging used by Azure Monitor
- Learn to Configure monitoring services
- Learn to Monitor stream processing
- Learn to Measure performance of data movement
- Learn to Monitor and update statistics about data across a system
- Learn to Monitor data pipeline performance
- Learn to Measure query performance
- Learn to Schedule and monitor pipeline tests
- Learn to Interpret Azure Monitor metrics and logs
- Learn to Implement a pipeline alert strategy
3.3 Optimize and troubleshoot data storage and data processing
- Learn to Compact small files
- Learn to Handle skew in data
- Learn to Handle data spill
- Learn to Optimize resource management
- Learn to Tune queries by using indexers
- Learn to Tune queries by using cache
- Learn to Troubleshoot a failed Spark job
- Learn to Troubleshoot a failed pipeline run, including activities executed in external services
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- The in-depth and exhaustive explanation for every question
- Reliable exam reports evaluating strengths and weaknesses
- Latest Questions with an updated version
- Tips & Tricks to crack the test
- Unlimited access
What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real-time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
