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Microsoft DP-203 Exam Topics, Blueprint and Syllabus

Data Engineering on Microsoft Azure

Last Update October 22, 2024
Total Questions : 341

Our Microsoft Certified: Azure Data Engineer Associate DP-203 exam questions and answers cover all the topics of the latest Data Engineering on Microsoft Azure exam, See the topics listed below. We also provide Microsoft DP-203 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Microsoft DP-203 resources to help you understand the topics covered in the exam, such as Microsoft Certified: Azure Data Engineer Associate video tutorials, DP-203 study guides, and DP-203 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.

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Microsoft DP-203 Exam Overview :

Exam Name Data Engineering on Microsoft Azure
Exam Code DP-203
Actual Exam Duration The duration of the Microsoft DP-203 exam is 180 minutes (3 hours).
What exam is all about Microsoft DP-203 is an exam that tests the knowledge and skills of candidates in designing and implementing data solutions using Microsoft Azure technologies. The exam covers various topics such as data storage, data processing, data integration, data security, and data governance. Candidates who pass the exam demonstrate their ability to design and implement data solutions that meet business requirements and comply with industry standards. The exam is intended for data engineers, data architects, and database administrators who work with Azure technologies.
Passing Score required The passing score required in the Microsoft DP-203 exam is 700 out of 1000. This means that you need to answer at least 70% of the questions correctly to pass the exam. The actual passing score may vary depending on the difficulty level of the exam and the number of questions included in it. It is important to note that the passing score is subject to change without prior notice, so it is best to check the official Microsoft website for the latest information.
Competency Level required Based on the official Microsoft DP-203 Exam page, the exam is designed for data engineers who are responsible for designing and implementing data storage solutions using Azure services. The exam measures the candidate's ability to design and implement data storage solutions, manage and monitor data processing, and implement security and compliance solutions. Therefore, the competency level required for the exam is intermediate to advanced, and candidates should have experience working with Azure services and data storage solutions. It is recommended that candidates have at least one year of experience working with Azure and data storage solutions before taking the exam.
Questions Format The Microsoft DP-203 exam consists of multiple-choice questions, drag and drop questions, and scenario-based questions. The exam may also include simulations and case studies.
Delivery of Exam The Microsoft DP-203 exam is a computer-based exam that is delivered through the Pearson VUE testing centers. It is a proctored exam, which means that a proctor will monitor the exam taker throughout the duration of the exam to ensure that the exam is taken fairly and without any cheating. The exam consists of multiple-choice questions and is timed, with a total of 120 minutes (2 hours) given to complete the exam.
Language offered English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia)
Cost of exam $165 USD
Target Audience The target audience for Microsoft DP-203 certification exam includes data engineers, data architects, and database administrators who are responsible for designing and implementing data solutions using Azure technologies. These professionals should have a strong understanding of data storage, data processing, and data security concepts, as well as experience working with Azure data services such as Azure SQL Database, Azure Cosmos DB, and Azure Data Factory. Additionally, individuals who are interested in pursuing a career in data engineering or data architecture can also benefit from taking the DP-203 exam.
Average Salary in Market The average salary for a Microsoft Certified Data Engineer is around $107,000 per year in the United States. However, the salary may vary depending on the location, experience, and industry.
Testing Provider You can visit the official Microsoft website or authorized training centers to register for the exam.
Recommended Experience I can provide you with the recommended experience for the Microsoft DP-203 exam. The Microsoft DP-203 exam is designed for data engineers who are responsible for designing and implementing data solutions using Azure services. The recommended experience for this exam includes: 1. Experience with Azure data services: Candidates should have experience working with Azure data services such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics. 2. Knowledge of data storage solutions: Candidates should have knowledge of data storage solutions such as Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database. 3. Understanding of data processing: Candidates should have an understanding of data processing concepts such as data ingestion, data transformation, and data integration. 4. Familiarity with programming languages: Candidates should be familiar with programming languages such as Python, SQL, and PowerShell. 5. Knowledge of data security: Candidates should have knowledge of data security concepts such as encryption, access control, and data masking. 6. Experience with data visualization: Candidates should have experience with data visualization tools such as Power BI. Overall, candidates should have a strong understanding of data engineering concepts and experience working with Azure data services.
Prerequisite The prerequisite for Microsoft DP-203 exam is a basic understanding of data engineering concepts and technologies, including data storage, data processing, and data transformation. Additionally, candidates should have experience working with Azure data services, such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. It is also recommended that candidates have experience with programming languages such as Python and SQL.
Retirement (If Applicable) Microsoft usually announces the retirement date of an exam at least six months in advance. It is recommended to check the Microsoft website or contact their support team for the latest information on the retirement date of the DP-203 exam.
Certification Track (RoadMap): The certification track or roadmap for the Microsoft DP-203 exam is as follows: 1. Microsoft Certified: Azure Data Engineer Associate: This certification validates the skills required to design and implement the management, monitoring, security, and privacy of data using Azure services. 2. Exam DP-203: Data Engineering on Microsoft Azure: This exam measures the candidate's ability to design and implement data storage solutions, data processing solutions, and data security solutions using Azure services. 3. Microsoft Certified: Azure Solutions Architect Expert: This certification validates the skills required to design and implement solutions that run on Microsoft Azure, including aspects like compute, storage, networking, and security. 4. Exam AZ-303: Microsoft Azure Architect Technologies: This exam measures the candidate's ability to design and implement solutions that run on Microsoft Azure, including aspects like compute, storage, networking, and security. 5. Exam AZ-304: Microsoft Azure Architect Design: This exam measures the candidate's ability to design and implement solutions that run on Microsoft Azure, including aspects like compute, storage, networking, and security. Overall, the certification track or roadmap for the Microsoft DP-203 exam is focused on data engineering and architecture on the Microsoft Azure platform.
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
See Expected Questions Microsoft DP-203 Expected Questions in Actual Exam
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Microsoft DP-203 Exam Topics :

Section Weight Objectives
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