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
Long-term cooperation with customers
If you enjoy a comfortable and satisfying purchasing service of DP-203 Deutsch test questions, we hope you can still choose us when you need other products. We pay important attention to honor and reputation, so it is our longtime duty to do better about our DP-203 Deutsch test engine, and that is what we are proud of. After receiving feedback of former customers, they inspired us and made us do better. They also recommend DP-203 Deutsch test questions to people around them. We earn this by accuracy of practice dumps, so do not need to worry about quality and trust us as friends who help you get over problems. We regard the pass of your test exam as our business, and send you intimate service. If you get a satisfying experience about DP-203 Deutsch test dumps this time, expect your preference next time.
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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
The features of three-type- products: PDF & Software & APP version
All these types of products are the newest version of authorized exam dumps materials for Microsoft Microsoft Certified: Azure Data Engineer Associate exam. You can tell according to updating version NO. on website. Here we want to introduce the DP-203 Deutsch set especially to you---A desirable version supporting browse on the web included many questions. You can pay only dozens of money for it with some discount. As the main provider of DP-203 Deutsch pass king materials, we recommend this kind of version to customers. When we updates questions, we shall instantly send you related details about DP-203 Deutsch test questions to you Email box, give customers heartfelt service, or you can contact with customer service for them. Besides the full refund guarantee, we also promise send you the latest DP-203 Deutsch test engine questions even you pass the test, so you can realize any tiny changes.
About our DP-203 Deutsch test questions, it is one of authorized test materials for candidates who hold ambitious aims in the area. So we give you a brief introduction of DP-203 Deutsch test engine as follows:
Some tips &Notice
During you practice with DP-203 Deutsch test questions, you can mark the most important and difficult points, and exchange them with friends, which can speed up you process and build up confidence, before get down to business, look through the whole contents of DP-203 Deutsch test engine quickly, which can help you be familiar with questions. Hope you can pass the Microsoft Microsoft Certified: Azure Data Engineer Associate test smoothly. After placing your order successfully, then you can download exam dumps or system will send you DP-203 Deutsch test questions in a few hours. Once you received our products, you just need to spend one or two days to practice questions and repeat the answers of DP-203 Deutsch pass king materials. (In case you do not receive any massage, please notice us at your available time, do not forget to check junk mailbox.)
After purchase, Instant Download: Upon successful payment, Our systems will automatically send the product you have purchased to your mailbox by email. (If not received within 12 hours, please contact us. Note: don't forget to check your spam.)
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 |
