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About Microsoft DP-203 Exam

Microsoft DP-203 is a qualifying exam for obtaining the Microsoft Certified: Azure Data Engineer Associate certification. This test evaluates the ability of the candidates to design & implement data storage as well as measures their skills in designing & implementing data security and designing & developing data processing.

The target specialists for this certification test are Azure Data Engineers. These individuals consolidate, transform, and integrate data from different unstructured and structured data systems into the standard structures suitable for creating analytics solutions. It is recommended that the potential candidates for the Microsoft DP-203 exam have the relevant knowledge and skills in data processing of various languages, including Scala, Python, or SQL. The learners should also possess a good understanding of data architecture patterns and parallel processing.

Exam Details

The Microsoft DP-203 exam is only available in the English language and can be taken in person at a Pearson VUE testing center or as a proctored delivered exam from anywhere in the world. It is recommended to check the official website to see if the proctored option is available in your country. This certification test contains about 40-60 questions and the candidates will be given 150 minutes to answer them. Please note that you will come across different question formats during the delivery of the exam. Some of the common types that you may find include multiple choice, fill-in-the-blank, drag and drop, case studies, best answer, active screen, and build list. To qualify for the associated certificate, you must achieve the passing score of 700 points on a scale of 1-1000. Getting this mark is not an easy task, so the learners must give most of their time to their preparation process if they want to ace Microsoft DP-203 at the first attempt. To help them in their preparation journey, the vendor offers two training options, which are the instructor-led course and free learning paths.

Data Engineering on Microsoft Azure is a paid course that covers the extensive scope of the exam content. The candidates who enroll for this training will gain an understanding of data engineering practices and patterns about working with the real-time analytical solutions that utilize Azure data platform technologies. They will also gain knowledge of storage technologies and core compute utilized in building analytical solutions. The students will also get the skills in interactively exploring data that are stored in files within a data lake. The instructor-led training course covers everything that the individuals need to know to achieve success in the test and also in the workplace. As for the online learning path, it covers ten different topics with specific modules. You can combine all these resources as well as other tools available at other platforms to ensure that they optimize the chance to nail the exam.

Exam Topics

The questions for the test will be drawn from the topics of the exam content. This is why the interested candidates must study the domains during the preparation process. The overview of the subject areas covered in Microsoft DP-203 is the following:

Data Storage Design & Implementation: 40-45%

  • Design of a structure for data storage: This area includes the details of the design of Azure Data Lake solutions, suggestion of file types for storage & analytical queries, as well as design for data pruning, efficient querying, and folder structure for data transformation levels. The learners should also have competence in designing data archiving solutions and distribution strategies;
  • Design of the partition strategy: The individuals require the competence in designing specific partition strategies for files, analytical workloads, performance/efficiency, and Azure Synapse Analytics. It is also required to have the ability to recognize when partitioning is needed within Azure Data Lake Storage Gen2;
  • Design of the serving layers: This subsection covers one’s competence in designing star schemas, temporal data solution, dimensional hierarchy, and incremental loading. The students should also demonstrate their skills in designing analytical stores, and meta-stores within Azure Synapses Analytics as well as Azure Databricks;
  • Logical data structures implementation: The subtopic covers the knowledge areas in building temporal data solutions, slowly changing dimensions, external tables, logical folder structure, and file & folder structures for data pruning & effective querying;
  • Physical data storage structures implementation: This objective covers the individuals’ skills in implementing compression, partitioning, sharding, and various table geometries with the use of the Azure Synapse Analytics pools. It also covers their skills in implementing distributions, data redundancy, and data archiving;
  • Serving layer implementation: This part requires your expertise in delivering data within the relational star schema, data within Parquet files, as well as implementing the dimensional hierarchy and maintaining metadata.

Data Processing Design & Development: 25-30%

  • Data ingestion & transformation: This subtopic covers the learners’ competence in transforming data with Apache Spar, Transact-SQL, Data Factory, Azure Synapse Pipeline, and Stream Analytics. It also focuses on the skills in cleansing data, shredding JSON, splitting data, encoding & decoding data, as well as configuring error handling for transformations;
  • Batch processing solution design & development: Here, the potential candidates should demonstrate their competence in developing batch processing solutions with Data Lake, Data Factory, Azure, Spark, PolyBase, Azure Databricks, and Synapse Pipeline;
  • Stream processing solution design & development: This area includes the applicants’ skills in developing stream processing solutions with Stream Analytics, Azure Event Hubs, and Azure Databricks. It also focuses on their competence in processing data with the Spark structured streaming and handling schema drift;
  • Batches & pipeline management: This module covers the skills in handling failed batch loads, triggering batches, and scheduling pipeline with the Synapse Pipelines/Data factory.

Data Security Design & Implementation: 10-15%

  • Designing security for data standards & policies: This objective covers your competence in designing data encryption for both data in transit & data at rest, data privacy, data masking strategy, data auditing strategy, and data retention policy;
  • Data security implementation: This topic covers data masking implementation and encrypting data in motion & data at rest.

Data Processing & Data Storage Monitoring and Optimization: 10-15%

  • Data processing & data storage monitoring: This area includes implementing logging utilized by Azure Monitor and measuring data movement performance;
  • Data processing & data storage optimization and troubleshooting: This subtopic includes the information about rewriting UDFs as well as handling data spills.

Career Opportunities

It is a known fact that the certified professionals can explore many career paths and job roles. Some of the options that these individuals can take up include an Associate Data Engineer, an Azure Administrator, an Azure Engineer, a DevOps Engineer, a Backend Software Engineer, and a Data Architect, among others. The average remuneration for the certificate holders is $96,000 per annum.


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