Try and practice the latest Microsoft : DP-203 Deutsch real questions & answers
Updated: Aug 30, 2026
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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
Where can I find good help with Microsoft DP-203 preparation?
If you want to learn Microsoft DP-203 exam preparation then the first thing is to find the best Microsoft DP-203 questions. You must be wondering where can you find the best Microsoft DP-203 questions for your DP-203 exam preparation. The best resource I have come across is the Microsoft Official Academic Course (MOAC) which is a series of courses that have been created by the same people who have been involved in the creation of the Microsoft DP-203 course. Microsoft DP-203 Dumps includes all the essential topics that are needed to answer a question on this exam. These are real instructors that are working with you every step of the way. It is like being back in school again but this time it would be better than ever before. It is like having your own classroom and not having to worry about all those other students around you. The MOAC allows you to learn at your own pace and it comes with a practice exam that helps you test your knowledge as you progress through each course. The practice exams allow you to test your knowledge and see where you need more studying so that you will know what topics to focus on more before taking the actual Microsoft DP-203 exam.
Microsoft DP-203 Deutsch Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Data Engineering on Microsoft Azure |
| Exam Number: | DP-203 |
| Exam Format: | Multiple choice, Case studies, Multiple response, Build list, Drag and drop |
| Related Certifications: | Microsoft Certified: Azure Data Fundamentals (DP-900) Microsoft Certified: Azure Solutions Architect Expert (AZ-305) |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | Chinese (Simplified), Korean, Portuguese (Brazil), French, English, Japanese, Spanish, German |
| Exam Price: | $165 USD (varies by country/region) |
| Real Exam Qty: | 40-60 (approx.) |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 1 year (renewable through free online assessment) |
| Recommended Training: | Microsoft Learn DP-203 Learning Path Microsoft Azure Data Engineer Associate Certification |
| Exam Registration: | Pearson VUE Registration Microsoft Learn Certification Exam Page |
| Sample Questions: | Microsoft DP-203 Deutsch Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers (Pearson VUE) |
| Pre Condition: | No mandatory prerequisites. Recommended: familiarity with Azure data services, SQL, and basic data engineering concepts. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-203/ |
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