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
Do you want to change while an acquaintance runs towards more promoting position? If you want to change, change yourself, change the boring career and life. Come with DP-203 pass-sure braindumps: Data Engineering on Microsoft Azure, get what you want. Defy the mediocre life. To a more interesting world with more challenges and defy the doleful life through Data Engineering on Microsoft Azure exam torrent. Do not go through your life unprepared. Remember that nothing can stop you running with joy. Believe DP-203 exam guide which will make you experience something different---a totally new world open for you. You should know that God helps people who help themselves. So you should seize DP-203 exam ---the opportunities by yourself.
What is Microsoft DP-203 Certification
Microsoft DP-203 (Designing Data Platform Solutions on Microsoft Azure) certification is a great way to validate your hands-on experience with cloud platforms, data architecture, and database technologies. This exam is designed to determine the competency of candidates to design distributed data solutions for enterprise applications based on Azure technologies. Before you begin studying for this exam, there are a few terms that you should familiarize yourself with. First and foremost, you should understand the difference between Microsoft SQL Server, Microsoft SQL Database, and Microsoft Azure SQL Database. Exam objectives are associated with these products; therefore, it is important to know when you will be tested on each one. Microsoft DP-203 Dumps tests a candidate's knowledge of Microsoft technologies used for building a data platform and cloud environment. Microsoft DP-203 is a certification of data engineers. The certification exam is designed to test the skills of designing, deploying, and managing Microsoft HDInsight and Microsoft Azure SQL Data Warehouse clusters for business intelligence and analytics solutions.
Totally new experience
With DP-203 pass-sure braindumps: Data Engineering on Microsoft Azure, study does not a hard work anymore. Almost all people who dislike study may because it's too boring and difficult. Well, DP-203 exam guide will give you the totally new experience of study. The DP-203 exam simulator is able to offer you a more interesting and easier way to attain relative knowledge. Actually, you may feel said when you fail to solve text items, on the contrary, you will have a sense of achievement when you settle down a tough problem. For that almost every question of DP-203 pass-sure braindumps: Data Engineering on Microsoft Azure is attached detailed explanation. Then DP-203 exam guide will provide you the opportunities to solve all questions to bring you such successful sense. Guess what? Yes, your interest of study will rise up definitely. As we say that interest is the best teacher, to say that the Data Engineering on Microsoft Azure exam pass-sure materials send the best study material to you. The DP-203 exam dump definitely is your trump card to become good at all the essential knowledge to pass the exam.
Advantages of PDF version
To satisfy your habit of learning by papers, the DP-203 pass-sure braindumps: Data Engineering on Microsoft Azure offers you the PDF version for you which are able to be printed out. And so it is that many leaners feel more comfortable to study on paper, with the PDF version of DP-203 exam guide you are able to do notes at your will. And these notes will make it easier for you to absorb the testing centers. The Data Engineering on Microsoft Azure exam pass-sure materials will show you the Microsoft certification can't be the tower of Babel for you, you can make it.
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.)
100% hit rate
We always say that three cobblers with their wits combined equal Chukeh Liang the master mind. Even the collective commons' wits are so strong moreover the DP-203 pass-sure braindumps: Data Engineering on Microsoft Azure which gathers the wits and experiences of the most powerful experts. After studying the materials of the DP-203 exam guide, you can see the capacity or the startling hit rate of the exam totally from its study items. You know what the high hit rate means, it equals to the promise of Microsoft certification. In short, it just like you're studying the real exam questions when you learn the Data Engineering on Microsoft Azure exam dump or you will definitely pass the exam if you have mastered all the knowledge in Data Engineering on Microsoft Azure exam torrent.
Familiarize yourself with the format of the Microsoft DP-203 Exam
Microsoft Data Platform (DP) is an exam for IT professionals who are responsible for designing and implementing data processing solutions that integrate with Microsoft platforms, applications, and services. Candidates prepare for this exam by taking the Microsoft Official Curriculum (MOC) course for the Azure Data Engineering on Microsoft Azure certification. The DP-203 exam tests your ability to design and implement data processing solutions on Azure in a cloud environment. You need to understand how to design and create data services with Azure Data Factory; how to create, manage and deploy data models using the Azure Data Catalog; and how to manage the lifecycle of a data solution using Azure Data Lake Analytics. Microsoft DP-203 Dumps Questions and Answers are prepared by experts and reviewed and approved by Microsoft. The test is based on the latest version of Microsoft Data Platform that includes SQL Server 2016, SQL Database, HDInsight, and Power BI. The questions test both technical skills and business knowledge so you need to have a good understanding of both areas in order to pass the test.
Virgil 
Jonas 
David 
Lester 
Abigail 
Kent 
Hazel 
Elton 
Gabriel 
Christ 
Lindsay 
Raymond 
Afra 
Pandora 
Doris 
Isidore 
Mignon 
Julia 
Kenneth 
