Why is it that important to be certified in the Microsoft DP-203 Exam?
The Microsoft Data Platform is evolving rapidly and expanding with Azure. The certification exams help you acquire the latest technologies and share your knowledge with others in the field. Getting these certifications has become a must-have badge as it creates your credibility in front of potential employers and clients. The exam covers topics like SQL Server 2014, Azure SQL Database, Azure SQL Data Warehouse, Analysis Services, and Reporting Services. The DP-203 exam is an entry-level exam that tests the candidates on their ability to choose the right tools and techniques to meet business requirements. Microsoft DP-203 Dumps is designed to help students gain hands-on experience and develop skills to pass the DP-203 exam and earn the Microsoft Data Platform Certification. The DP-203 exam will be available in English only, at Prometric test centers globally. Before appearing for the exam make sure you prepare well by checking out our study guide and practice questions based on real-time scenarios to gain good marks for this exam.
Microsoft DP-203 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Design and Implement Data Storage (40-45%) | |
| Design a data storage structure | - design an Azure Data Lake solution - recommend file types for storage - recommend file types for analytical queries - design for efficient querying - design for data pruning - design a folder structure that represents the levels of data transformation - design a distribution strategy - design a data archiving solution |
| Design a partition strategy | - design a partition strategy for files - design a partition strategy for analytical workloads - design a partition strategy for efficiency/performance - design a partition strategy for Azure Synapse Analytics - identify when partitioning is needed in Azure Data Lake Storage Gen2 |
| Design the serving layer | - design star schemas - design slowly changing dimensions - design a dimensional hierarchy - design a solution for temporal data - design for incremental loading - design analytical stores - design metastores in Azure Synapse Analytics and Azure Databricks |
| Implement physical data storage structures | - implement compression - implement partitioning - implement sharding - implement different table geometries with Azure Synapse Analytics pools - implement data redundancy - implement distributions - implement data archiving |
| Implement logical data structures | - build a temporal data solution - build a slowly changing dimension - build a logical folder structure - build external tables - implement file and folder structures for efficient querying and data pruning |
| Implement the serving layer | - deliver data in a relational star schema - deliver data in Parquet files - maintain metadata - implement a dimensional hierarchy |
Design and Develop Data Processing (25-30%) | |
| Ingest and transform data | - transform data by using Apache Spark - transform data by using Transact-SQL - transform data by using Data Factory - transform data by using Azure Synapse Pipelines - transform data by using Stream Analytics - cleanse data - split data - shred JSON - encode and decode data - configure error handling for the transformation - normalize and denormalize values - transform data by using Scala - perform data exploratory analysis |
| Design and develop a batch processing solution | - develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks - create data pipelines - design and implement incremental data loads - design and develop slowly changing dimensions - handle security and compliance requirements - scale resources - configure the batch size - design and create tests for data pipelines - integrate Jupyter/Python notebooks into a data pipeline - handle duplicate data - handle missing data - handle late-arriving data - upsert data - regress to a previous state - design and configure exception handling - configure batch retention - design a batch processing solution - debug Spark jobs by using the Spark UI |
| Design and develop a stream processing solution | - develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs - process data by using Spark structured streaming - monitor for performance and functional regressions - design and create windowed aggregates - handle schema drift - process time series data - process across partitions - process within one partition - configure checkpoints/watermarking during processing - scale resources - design and create tests for data pipelines - optimize pipelines for analytical or transactional purposes - handle interruptions - design and configure exception handling - upsert data - replay archived stream data - design a stream processing solution |
| Manage batches and pipelines | - trigger batches - handle failed batch loads - validate batch loads - manage data pipelines in Data Factory/Synapse Pipelines - schedule data pipelines in Data Factory/Synapse Pipelines - implement version control for pipeline artifacts - manage Spark jobs in a pipeline |
Design and Implement Data Security (10-15%) | |
| Design security for data policies and standards | - design data encryption for data at rest and in transit - design a data auditing strategy - design a data masking strategy - design for data privacy - design a data retention policy - design to purge data based on business requirements - design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2 - design row-level and column-level security |
| Implement data security | - implement data masking - encrypt data at rest and in motion - implement row-level and column-level security - implement Azure RBAC - implement POSIX-like ACLs for Data Lake Storage Gen2 - implement a data retention policy - implement a data auditing strategy - manage identities, keys, and secrets across different data platform technologies - implement secure endpoints (private and public) - implement resource tokens in Azure Databricks - load a DataFrame with sensitive information - write encrypted data to tables or Parquet files - manage sensitive information |
Monitor and Optimize Data Storage and Data Processing (10-15%) | |
| Monitor data storage and data processing | - implement logging used by Azure Monitor - configure monitoring services - measure performance of data movement - monitor and update statistics about data across a system - monitor data pipeline performance - measure query performance - monitor cluster performance - understand custom logging options - schedule and monitor pipeline tests - interpret Azure Monitor metrics and logs - interpret a Spark directed acyclic graph (DAG) |
| Optimize and troubleshoot data storage and data processing | - compact small files - rewrite user-defined functions (UDFs) - handle skew in data - handle data spill - tune shuffle partitions - find shuffling in a pipeline - optimize resource management - tune queries by using indexers - tune queries by using cache - optimize pipelines for analytical or transactional purposes - optimize pipeline for descriptive versus analytical workloads - troubleshoot a failed spark job - troubleshoot a failed pipeline run |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
How someone with a Microsoft DP-203 certificate will be better off?
There is no doubt that the DP-203 certificate on Microsoft Azure will be helpful in showing future employers and clients that you have a good understanding of the Microsoft Azure platform and have a sound knowledge of data management, data processing, and business intelligence. You can use this DP-203 certification to demonstrate your ability to build an enterprise-class data warehousing solution using Microsoft Azure's fully managed services. Microsoft DP-203 Dumps is the best way to ensure that you pass the exam on the first attempt. With these Microsoft DP-203 Practice Tests, you will be able to test your preparation before the real exam. After completing this course, you will be able to: Describe the challenges for data warehousing in the cloud. Understand how cloud storage works with Azure SQL Data Warehouse. Implement a relational database in the cloud using Azure SQL Database Managed Instance. Deploy a highly available and scalable data warehouse using Azure SQL Data Warehouse. External workloads load efficient nodes repartitioning folder selection guides duplicate hierarchy. Loading, archiving, pruning, premises, tabular, defined dimensional purposes. Stream table pipelines distribution handling control region temporal incremental dimensions structure tool. Demo PDF is also available.
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Exam DP-203: Data Engineering on Microsoft Azure
Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.
Azure Data Engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.
Azure Data Engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.
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.
Part of the requirements for: Microsoft Certified: Azure Data Engineer Associate
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