Easy Purchase Process
Please don't worry about the purchase process because it's really simple for you. The first step is to select the DP-203 Korean test guide, choose your favorite version, the contents of different version are the same, but different in their ways of using. The second step: fill in with your email and make sure it is correct, because we send our Data Engineering on Microsoft Azure (DP-203 Korean Version) learn tool to you through the email. Later, if there is an update, our system will automatically send you the latest Data Engineering on Microsoft Azure (DP-203 Korean Version) version. At the same time, choose the appropriate payment method, such as SWREG, DHpay, etc. Next, enter the payment page, it is noteworthy that we only support credit card payment, do not support debit card. Generally, the system will send the DP-203 Korean certification material to your mailbox within 10 minutes. If you don't receive it please contact our after-sale service timely.
Reliable Data Engineering on Microsoft Azure (DP-203 Korean Version) Study Materials
For customers who are bearing pressure of work or suffering from career crisis, Data Engineering on Microsoft Azure (DP-203 Korean Version) learn tool of inferior quality will be detrimental to their life, render stagnancy or even cause loss of salary. So choosing appropriate DP-203 Korean test guide is important for you to pass the exam. One thing we are sure, that is our DP-203 Korean certification material is reliable. With our high-accuracy DP-203 Korean test guide, our candidates can grasp the key points, and become sophisticated with the exam content. You only need to spend 20-30 hours practicing with our Data Engineering on Microsoft Azure (DP-203 Korean Version) learn tool, passing the exam would be a piece of cake.
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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
Fabulous Pass Rate
We attract customers by our fabulous DP-203 Korean certification material and high pass rate, which are the most powerful evidence to show our strength. We are so proud to tell you that according to the statistics from our customers' feedback, the pass rate among our customers who prepared for the exam with our DP-203 Korean test guide have reached as high as 99%, which definitely ranks the top among our peers. Hence one can see that the Data Engineering on Microsoft Azure (DP-203 Korean Version) learn tool compiled by our company are definitely the best choice for you.
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 |
It is believe that employers nowadays are more open to learn new knowledge, as they realize that Microsoft certification may be conducive to them in refreshing their life, especially in their career arena. A professional Microsoft certification serves as the most powerful way for you to show your professional knowledge and skills. For those who are struggling for promotion or better job, they should figure out what kind of DP-203 Korean test guide is most suitable for them. However, some employers are hesitating to choose. We here promise you that our DP-203 Korean certification material is the best in the market, which can definitely exert positive effect on your study. Our Data Engineering on Microsoft Azure (DP-203 Korean Version) learn tool create a kind of relaxing leaning atmosphere that improve the quality as well as the efficiency, on one hand provide conveniences, on the other hand offer great flexibility and mobility for our customers. That's the reason why you should choose us.
Microsoft DP-203 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Develop data processing | 40-45% | - Ingest and transform data
|
| Monitor and optimize data solutions | 30-35% | - Monitor data processing
|
| Secure data solutions | - Ensure compliance
| |
| Design and implement data storage | 25-30% | - Develop data storage solutions
|
0 Customer Reviews
