DP-203: Microsoft Azure Data Engineering Practice Tests 2025

About DP-203: Microsoft Azure Data Engineering Practice Tests 2025

Microsoft Azure DP-203 Data Engineering Certification Practice Exam is a highly beneficial product for individuals seeking to enhance their proficiency in data engineering. This practice exam is designed to provide a comprehensive assessment of the candidate's knowledge and skills in data engineering, specifically in the context of Microsoft Azure. The exam is structured to simulate the actual certification exam, thereby enabling candidates to familiarize themselves with the format and content of the certification exam. This practice exam is designed to cover a wide range of topics, including data storage, data processing, data transformation, data integration, and data analysis. It is intended to evaluate the candidate's ability to design and implement data solutions using Microsoft Azure technologies. The exam is also designed to assess the candidate's ability to optimize data solutions for performance, scalability, and reliability. Microsoft Azure DP-203 Data Engineering Certification Practice Exam is an excellent tool for individuals seeking to validate their skills and knowledge in data engineering. It provides a realistic simulation of the certification exam, thereby enabling candidates to identify areas of weakness and focus their efforts on improving their performance. Additionally, the practice exam is an effective means of preparing for the certification exam, as it provides candidates with a comprehensive understanding of the exam format and content.

Data Engineering on Microsoft Azure Exam Summary:

Number of Questions:

Maximum of 40-60 questions,

Type of Questions:

Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

Length of Test:

150 Minutes. The exam is available in English and Japanese languages.

Passing Score:

700 / 1000

Languages :

English at launch. Japanese

Schedule Exam :

Pearson VUE

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.

Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

Azure data engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.

A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

Microsoft Azure DP-203 Data Engineering Syllabus: :

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

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 Overall, the Microsoft Azure DP-203 Data Engineering Certification Practice Exam is a highly valuable product for individuals seeking to enhance their proficiency in data engineering. It provides a comprehensive assessment of the candidate's knowledge and skills, and is an effective means of preparing for the certification exam.