Bearable cost

We have to admit that the Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam certification is difficult to get, while the exam fees is very expensive. So, some people want to prepare the test just by their own study and with the help of some free resource. They do not want to spend more money on any extra study material. But the exam time is coming, you may not prepare well. Here, I think it is a good choice to pass the exam at the first time with help of the Data Engineering on Microsoft Azure (DP-203 Deutsch Version) actual questions & answer rather than to take the test twice and spend more money, because the money spent on the Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam dumps must be less than the actual exam fees. Besides, we have the money back guarantee that you will get the full refund if you fail the exam. Actually, you have no risk and no loss. Actually, the price of our Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam study guide is very reasonable and affordable which you can bear. In addition, we provide one year free update for you after payment. You don't spend extra money for the latest version. What a good thing.

At last, I want to say that our Microsoft Certified: Azure Data Engineer Associate Data Engineering on Microsoft Azure (DP-203 Deutsch Version) actual test is the best choice for your 100% success.

Microsoft DP-203 Deutsch braindumps Instant Download: Our system will send you the DP-203 Deutsch braindumps file you purchase in mailbox in a minute after payment. (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

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203

Customizable experience from Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine

Most IT candidates prefer to choose Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine rather than the pdf format dumps. After all, the pdf dumps have some limits for the people who want to study with high efficiency. DP-203 Deutsch Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine is an exam test simulator with customizable criteria. The questions are occurred randomly which can test your strain capacity. Besides, score comparison and improvement check is available by Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine, that is to say, you will get score and after each test, then you can do the next study plan according to your weakness and strengths. Moreover, the Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine is very intelligent, allowing you to set the probability of occurrence of the wrong questions. Thus, you can do repetition training for the questions which is easy to be made mistakes. While the interface of the test can be set by yourself, so you can change it as you like, thus your test looks like no longer dull but interesting. In addition, the Microsoft Certified: Azure Data Engineer Associate Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine can be installed at every electronic device without any installation limit. You can install it on your phone, doing the simulate test during your spare time, such as on the subway, waiting for the bus, etc. Finally, I want to declare the safety of the Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine. Data Engineering on Microsoft Azure (DP-203 Deutsch Version) test engine is tested and verified malware-free software, which you can rely on to download and installation.

Certification Topics of Microsoft DP-203 Exam

• Design and implement data storage (40-45%)

• Monitor and optimize data storage and data processing (10-15%)

• Design and develop data processing (25-30%)

• Design and implement data security (10-15%)

What is the cost of the Microsoft DP-203 Exam

The Microsoft DP-203 Exam cost is $165 USD.

Because of the demand for people with the qualified skills about Microsoft Data Engineering on Microsoft Azure (DP-203 Deutsch Version) certification and the relatively small supply, Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam certification becomes the highest-paying certification on the list this year. While, it is a tough certification for passing, so most of IT candidates feel headache and do not know how to do with preparation. In fact, most people are ordinary person and hard workers. The only way for getting more fortune and living a better life is to work hard and grasp every chance as far as possible. Gaining the DP-203 Deutsch Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam certification may be one of their drams, which may make a big difference on their life. As a responsible IT exam provider, our Data Engineering on Microsoft Azure (DP-203 Deutsch Version) exam prep training will solve your problem and bring you illumination.

What is the salary of a Microsoft DP-203 certified professional?

The Average salary of different countries of Microsoft DP-203 Certified professional

• United States - $104,000 USD

• UK - 78707 Pounds

• India - 7905404 INR