Free Demo
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 |
Learn about the benefits of Microsoft DP-203 Certification
Microsoft DP-203 certification is a professional certification given to the candidates who successfully complete the DP-203 exam. Microsoft Data Platform with Hadoop Developer 203: Administration certification is an international standard for demonstrating competence in data platform administration. The exam validates the candidate's ability to administer and develop data platforms on the cloud-based environment of Microsoft Azure. The DP-203 certification is a globally recognized credential that can enable you to stand out from your peers and make your career more rewarding. The DP-203 course will help you to become a specialist who is able to manage, maintain and develop applications running on Hadoop frameworks on the Azure cloud platform. Microsoft DP-203 Dumps is designed to achieve your goal. The DP-203 training course covers the fundamental concepts of cloud computing, creating and managing virtual machines, storage accounts, load balancers, web and worker roles, databases, HDInsight, etc. It also covers how to implement security infrastructure and management of virtual networks using PowerShell commands. You will receive lifetime access to the content along with practice exam questions from real exams after each module. The DP-203 course provides an opportunity for career advancement as it enables you to enhance your expertise in developing solutions with the Hadoop framework and other data sources using the Microsoft Azure cloud platform. It will also help you boost your proficiency in implementing. Correct mapping and auditing exception testing for data.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
Many young IT working people have their life attitude of upward and extraordinary, (DP-203 brain dumps) they regard IT certification Microsoft Microsoft Certified: Azure Data Engineer Associate as an important & outstanding advantage while they have better opportunities. However Microsoft DP-203 exam become an obstacle to going through the IT exams. They are urgent to gain a valid Data Engineering on Microsoft Azure brain dumps or Data Engineering on Microsoft Azure dumps pdf so that they can go through a pass and then do something interesting. Although there is so much information about Data Engineering on Microsoft Azure brain dumps or Data Engineering on Microsoft Azure dumps pdf, they find it difficult to find the valid and reliable website about IT real test. Now it is your good chance. Our Braindumpsit is the leading provider which offers you the best, valid and accurate Data Engineering on Microsoft Azure brain dumps & Data Engineering on Microsoft Azure dumps pdf. We can help you pass exam surely.
In the past several years our Data Engineering on Microsoft Azure brain dumps totally assisted more than 100000+ candidates to sail through the examinations, our passing rate of Data Engineering on Microsoft Azure dumps pdf is high up to 98.54%. Most of candidates would purchase IT exam cram from us second times. Customers think highly of our DP-203 brain dumps. We aim to make sure all our brain dumps pdf are high-quality because we have more than ten years' experienced education staff and professional IT staff. That's why our Data Engineering on Microsoft Azure brain dumps can have good reputation in this area. Besides, we not only offer valid & high-quality IT exam cram but also our service is also praise by most candidates.
Firstly, many candidates who purchased our DP-203 brain dumps said that we replied news and email fast. Yes, we have professional service staff working as a 24-7 on-line service. We request any on-line news or emails about DP-203 brain dumps or Data Engineering on Microsoft Azure dumps pdf should be replied and handled successfully in two hours. Be polite, patience and hospitable are the basic professional quality of our customer service staff.
Secondly, we guarantee you 100% pass the IT certification Data Engineering on Microsoft Azure exam for sure if you purchase our DP-203 brain dumps or Data Engineering on Microsoft Azure dumps pdf. Most candidates can pass exam once, but if you fail the exam we will serve for you until you pass. We have one-year service warranty; we will send you the update version of Data Engineering on Microsoft Azure brain dumps all the time within one year. If you fail the exam and give up, you want a refund we will refund the full money you paid us about Data Engineering on Microsoft Azure dumps pdf. We guarantee your money and information safety. No Pass No Pay! Please rest assured!
Thirdly, we have three versions of DP-203 brain dumps. Many candidates are not sure how to choose it. The great majority of customers choose the APP on-line test engine version of Data Engineering on Microsoft Azure brain dumps because it is multifunctional and stable in use. Also some customers are purchasing for their companies they will choose all the three versions of Data Engineering on Microsoft Azure brain dumps so that they can satisfy all people's characters.
Fourthly, as for the payment of DP-203 brain dumps or Data Engineering on Microsoft Azure dumps pdf, normally we just only support Credit Card with a credit card. The debit card is only available for only a very few countries. Credit Card is widely used in international trade business and is safe and stable for both buyer and seller. Also if you fail exam with our Data Engineering on Microsoft Azure brain dumps and apply for refund, it is also convenient for you.
All in all, our Data Engineering on Microsoft Azure brain dumps & Data Engineering on Microsoft Azure dumps pdf will certainly assist you go through exam and gain success of IT certification Microsoft Microsoft Certified: Azure Data Engineer Associate. If you give us trust we will give you a pass. Braindumpsit DP-203 brain dumps will be your lucky choice.
