Exam DP-203: Data Engineering on Microsoft Azure

Candidates who are proficient in data processing languages should take the DP-203: Data Engineering on Microsoft Azure exam. SQL, Python, or Scala are all options. Parallel processing and data architecture patterns must be recognisable to them. They must also be capable of integrating, converting, and combining data from a variety of structured and unstructured data systems into a framework that can be used to construct analytics solutions.

However, passing this exam will help candidates in becoming Microsoft Certified: Azure Data Engineer Associate.

Azure Data Engineer: Role and Responsibilities

• Firstly, Azure Data Engineers help stakeholders in understanding the data through exploration.
• Secondly, they have the skills to build and maintain secure and compliant data processing pipelines by using different tools and techniques. 
• Thirdly, they have familiarity with Azure data services and languages for storing and producing datasets for analysis.
• Fourthly, they ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable.
• Lastly, they handle unanticipated issues swiftly and minimize data loss. And, they are also responsible for designing, implementing, monitoring, and optimizing data platforms for meeting the data pipeline’s needs.

Exam Learning Path

Microsoft provides access to its learning path designed according to the exam. These learning paths consist of topics that contain various modules with details about the concept. Candidates can explore these modules to understand the concepts. For the Microsoft DP-203 exam, the modules include:

• Firstly, Azure for the Data Engineer
• Secondly, storing data in Azure
• Thirdly, Data integration at scale with Azure Data Factory or Azure Synapse Pipeline
• Next, using Azure Synapse Analytics for integrated Analytical Solutions and working with data warehouses 
• Then, performing data engineering using Azure Synapse Apache Spark Pools
• After that, Hybrid Transactional and Analytical Processing Solutions working process with using Azure Synapse Analytics
• Next, Data engineering with Azure Databricks
• Them, large-Scale Data Processing with Azure Data Lake Storage Gen2
• Lastly, implementing a Data Streaming Solution with Azure Streaming Analytics

Microsoft DP-203 Exam Details

Microsoft DP-203 exam will have 40-60 questions that can be in a format like scenario-based single answer questions, multiple-choice questions, arranged in the correct sequence type questions, or drop type of questions. There will be a time limit of 130 minutes to complete the exam and the passing score is a minimum of 700. Further, the Microsoft DP-203 exam will cost $165 USD and the exam can be taken in only the English language.

Scheduling Exam

Microsoft DP-203 exam measures the ability to perform tasks like designing and implementing data storage with developing data processing. And monitoring and optimizing data storage and data processing. However, for scheduling the DP-203 exam, candidates can log in to their Microsoft account and fill in the details. 

Microsoft DP-203 Exam Course Outline

Microsoft provides a course outline for the DP-203 exam that covers the major sections for getting better understanding during the preparation time. The topics are:

Topic 1: Design and Implement Data Storage

Design a data storage structure

• design an Azure Data Lake solution (Microsoft Documentation: Azure Data Lake Storage Gen2)
• recommend file types for storage (Microsoft Documentation: Example scenarios)
• recommend file types for analytical queries (Microsoft Documentation: Query data in Azure Data Lake using Azure Data Explorer)
• design for efficient querying (Microsoft Documentation: Design for querying, Design Guidelines)
• design for data pruning (Microsoft Documentation: Dynamic file pruning)
• designing a folder structure that represents the levels of data transformation (Microsoft Documentation: Copying and transforming data in Azure Data Lake Storage Gen2)
• design a distribution strategy (Microsoft Documentation: Designing distributed tables)
• designing a data archiving solution

Design a partition strategy

• design a partition strategy for files (Microsoft Documentation: Copy new files based on time partitioned file name using the Copy Data tool)
• designing a partition strategy for analytical workloads (Microsoft Documentation: Best practices when using Delta Lake, Partitions in tabular models)
• design a partition strategy for efficiency/performance (Microsoft Documentation: Designing partitions for query performance)
• designing a partition strategy for Azure Synapse Analytics (Microsoft Documentation: Partitioning tables)
• identify when partitioning is needed in Azure Data Lake Storage Gen2

Design the serving layer

• design star schemas (Microsoft Documentation: Overview of Star schema)
• designing slowly changing dimensions
• design a dimensional hierarchy (Microsoft Documentation: Hierarchies in tabular models)
• design a solution for temporal data (Microsoft Documentation: Temporal tables in Azure SQL Database and Azure SQL Managed Instance)
• designing for incremental loading (Microsoft Documentation: Incrementally load data from a source data store to a destination data store, Load data from Azure SQL Database to Azure Blob storage using the Azure portal)
• design analytical stores (Microsoft Documentation: Selecting an analytical data store in Azure, Azure Cosmos DB analytical store)
• designing metastores in Azure Synapse Analytics and Azure Databricks (Microsoft Documentation: Azure Synapse Analytics shared metadata tables)

Implement physical data storage structures

• implement compression (Microsoft Documentation: Data compression Overview)
• implementing partitioning (Microsoft Documentation: Overview of Data partitioning strategies)
• implement sharding (Microsoft Documentation: What is Sharding pattern, Adding a shard using Elastic Database tools)
• implement different table geometries with Azure Synapse Analytics pools (Microsoft Documentation: Defining Spatial Types – geometry (Transact-SQL), Table data types for dedicated SQL pool (formerly SQL DW) in Azure Synapse Analytics)
• implementing data redundancy (Microsoft Documentation: Overview of Azure Storage redundancy, Process of how storage account is replicated)
• implement distributions (Microsoft Documentation: Distributions overview, Table distribution Examples)
• implementing data archiving (Microsoft Documentation: Archive on-premises data to the cloud, Rehydrate blob data)

Implement logical data structures

• build a temporal data solution (Microsoft Documentation: Creating a system-versioned temporal table)
• building a slowly changing dimension
• build a logical folder structure
• build external tables (Microsoft Documentation: Using external tables with Synapse SQL, Create and alter external tables in Azure Storage or Azure Data Lake)
• implement file and folder structures for efficient querying and data pruning (Microsoft Documentation: Query multiple files or folders, Query folders and multiple files)

Implement the serving layer

• deliver data in a relational star schema
• deliver data in Parquet files (Microsoft Documentation: Parquet format in Azure Data Factory
• maintain metadata (Microsoft Documentation: Preserve metadata and ACLs using copy activity in Azure Data Factory)
• implement a dimensional hierarchy (Microsoft Documentation: Create and manage hierarchies)

Topic 2: Design and Develop Data Processing

Ingest and transform data

• transform data by using Apache Spark (Microsoft Documentation: Transform data in the cloud by using a Spark activity)
• transform data by using Transact-SQL (Microsoft Documentation: SQL Transformation)
• transforming data by using Data Factory (Microsoft Documentation: Transform data in Azure Data Factory)
• transform data by using Azure Synapse Pipelines (Microsoft Documentation: Transform data using mapping data flows)
• transform data by using Stream Analytics
• cleanse data (Microsoft Documentation: Overview of Data Cleansing, Clean Missing Data module)
• split data (Microsoft Documentation: Split Data Overview, Split Data module)
• shred JSON
• encode and decode data
• configure error handling for the transformation (Microsoft Documentation: Handle SQL truncation error rows in Data Factory, Troubleshoot mapping data flows in Azure Data Factory)
• normalize and denormalize values (Microsoft Documentation: Overview of Normalize Data module, What is Normalize Data?)
• transform data by using Scala (Microsoft Documentation: Extract, transform, and load data by using Azure Databricks)
• perform data exploratory analysis (Microsoft Documentation: Query data in Azure Data Explorer Web UI)

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 (Microsoft Documentation: What is Batch processing, Choosing a batch processing technology in Azure, Process large-scale datasets)
• create data pipelines (Microsoft Documentation: Creating a pipeline, Build a data pipeline)
• design and implement incremental data loads (Microsoft Documentation: Load data from Azure SQL Database to Azure Blob storage)
• design and develop slowly changing dimensions
• handle security and compliance requirements (Microsoft Documentation: Azure security baseline for Batch, Azure Policy Regulatory Compliance controls)
• scale resources (Microsoft Documentation: Create an automatic formula for scaling compute nodes)
• configure the batch size (Microsoft Documentation: Selecting VM size and image for compute nodes)
• design and create tests for data pipelines
• integrate Jupyter/IPython notebooks into a data pipeline (Microsoft Documentation: Set up a Python development environment for Azure Machine Learning, Azure Machine Learning with Jupyter Notebooks)
• handle duplicate data (Microsoft Documentation: Handling duplicate data in Azure Data Explorer, Removing Duplicate Rows module)
• handling missing data (Microsoft Documentation: Cleaning Missing Data module)
• handle late-arriving data (Microsoft Documentation: Understand time handling in Azure Stream Analytics, Time Skew Policies)
• upsert data
• regress to a previous state (Microsoft Documentation: Monitor Batch solutions by counting tasks and nodes by state)
• design and configure exception handling (Microsoft Documentation: Azure Batch error handling and detection)
• configure batch retention (Microsoft Documentation: Azure Batch best practices)
• design a batch processing solution (Microsoft Documentation: Overview of Batch processing)
• debug Spark jobs by using the Spark UI (Microsoft Documentation: Debug Apache Spark jobs running on Azure HDInsight)

Design and develop a stream processing solution

• develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs (Microsoft Documentation: Stream processing with Azure Databricks, Stream data into Azure Databricks using Event Hubs)
• process data by using Spark structured streaming (Microsoft Documentation: What is Structured Streaming? Apache Spark Structured Streaming)
• monitor for performance and functional regressions (Microsoft Documentation: Stream Analytics job monitoring and process to monitor queries)
• design and create windowed aggregates (Microsoft Documentation: Stream Analytics windowing functions, Windowing functions)
• handle schema drift (Microsoft Documentation: Schema drift in mapping data flow)
• process time-series data (Microsoft Documentation: Time handling in Azure Stream Analytics, What is Time series solutions?)
• processing across partitions (Microsoft Documentation: Stream processing with Azure Stream Analytics, Optimize processing with Azure Stream Analytics using repartitioning)
• the process within one partition
• configure checkpoints/watermarking during processing (Microsoft Documentation: Checkpoint and replay concepts, Example of watermarks)
• scale resources (Microsoft Documentation: Streaming Units, Scale an Azure Stream Analytics job)
• design and create tests for data pipelines (Microsoft Documentation: Testing live data locally, Test an Azure Stream Analytics job)
• optimize pipelines for analytical or transactional purposes (Microsoft Documentation: Query parallelization in Azure Stream Analytics, Optimize processing with Azure Stream Analytics using repartitioning)
• handle interruptions (Microsoft Documentation: Stream Analytics job reliability during service updates)
• design and configure exception handling (Microsoft Documentation: Output error policy, User-defined functions in Azure Stream Analytics)
• upsert data (Microsoft Documentation: Azure Stream Analytics output to Azure Cosmos DB)
• replay archived stream data (Microsoft Documentation: Checkpoint and replay concepts)
• design a stream processing solution (Microsoft Documentation: Stream processing)

Manage batches and pipelines

• trigger batches (Microsoft Documentation: Trigger a Batch job using Azure Functions)
• handle failed batch loads (Microsoft Documentation: Check for pool and node errors)
• validate batch loads (Microsoft Documentation: Error checking for job and task)
• manage data pipelines in Data Factory/Synapse Pipelines (Microsoft Documentation: Managing the mapping data flow graph)
• schedule data pipelines in Data Factory/Synapse Pipelines (Microsoft Documentation: Create a trigger)
• implement version control for pipeline artifacts (Microsoft Documentation: Source control in Azure Data Factory)
• manage Spark jobs in a pipeline (Microsoft Documentation: Monitor a pipeline)

DP-203 Interview Questions

Topic 3: Design and Implement Data Security

Design security for data policies and standards

• design data encryption for data at rest and in transit (Microsoft Documentation: Azure Data Encryption at rest, Data in transit)
• designing a data auditing strategy (Microsoft Documentation: Auditing for Azure SQL Database and Azure Synapse Analytics)
• design a data masking strategy (Microsoft Documentation: Overview of Dynamic data masking)
• design for data privacy
• designing a data retention policy (Microsoft Documentation: Understand data retention in Azure Time Series Insights Gen1)
• design to purge data based on business requirements (Microsoft Documentation: Enable data purge, Overview of Data purge)
• designing Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2 (Microsoft Documentation: Access control model in Azure Data Lake Storage Gen2, Access control lists (ACLs))
• design row-level and column-level security (Microsoft Documentation: Overview of Column-level security)

Implement data security

• implement data masking (Microsoft Documentation: SQL Database dynamic data masking with the Azure portal)
• encrypt data at rest and in motion (Microsoft Documentation: Transparent data encryption for SQL Database, SQL Managed Instance, and Azure Synapse Analytics)
• implement row-level and column-level security
• implementing Azure RBAC (Microsoft Documentation: Azure portal for assigning an Azure role for access to blob and queue data)
• implement POSIX-like ACLs for Data Lake Storage Gen2 (Microsoft Documentation: PowerShell for managing directories and files in Azure Data Lake Storage Gen2)
• implement a data retention policy (Microsoft Documentation: Configuring retention in Azure Time Series Insights Gen1)
• implementing a data auditing strategy (Microsoft Documentation: Auditing for Azure SQL Database and Azure Synapse Analytics)
• manage identities, keys, and secrets across different data platform technologies
• implement secure endpoints (private and public) (Microsoft Documentation: Private endpoints for Azure Storage, Azure SQL Managed Instance securely with public endpoints, Configure public endpoint)
• implement resource tokens in Azure Databricks (Microsoft Documentation: Authentication using Azure Databricks personal access tokens)
• load a DataFrame with sensitive information (Microsoft Documentation: Overview of DataFrames)
• write encrypted data to tables or Parquet files
• manage sensitive information (Microsoft Documentation: Explaining Security Control: Data Protection)

Topic 4: Monitor and Optimize Data Storage and Data Processing

Monitor data storage and data processing

• implement logging used by Azure Monitor (Microsoft Documentation: Overview of Azure Monitor Logs, Collecting custom logs with Log Analytics agent in Azure Monitor)
• configure monitoring services (Microsoft Documentation: Monitoring Azure resources with Azure Monitor, Define Enable VM insights)
• measure performance of data movement (Microsoft Documentation: Overview of Copy activity performance and scalability)
• monitor and update statistics about data across a system (Microsoft Documentation: Statistics in Synapse SQL, UPDATE STATISTICS)
• monitor data pipeline performance (Microsoft Documentation: Monitor and Alert Data Factory by using Azure Monitor)
• measure query performance (Microsoft Documentation: Query Performance Insight for Azure SQL Database)
• monitor cluster performance (Microsoft Documentation: Monitor cluster performance in Azure HDInsight)
• understand custom logging options (Microsoft Documentation: Collecting custom logs with Log Analytics agent in Azure Monitor)
• schedule and monitor pipeline tests (Microsoft Documentation: Monitor and manage Azure Data Factory pipelines by using the Azure portal and PowerShell)
• interpret Azure Monitor metrics and logs (Microsoft Documentation: Overview of Azure Monitor Metrics, Define Azure platform logs)
• interpret a Spark directed acyclic graph (DAG)

Optimize and troubleshoot data storage and data processing

• compact small files (Microsoft Documentation: Explain Auto Optimize)
• rewrite user-defined functions (UDFs) (Microsoft Documentation: Process of modifying User-defined Functions)
• handle skew in data (Microsoft Documentation: Resolve data-skew problems by using Azure Data Lake Tools for Visual Studio)
• handle data spill
• tune shuffle partitions
• find shuffling in a pipeline
• optimize resource management
• tune queries by using indexers (Microsoft Documentation: Automatic tuning in Azure SQL Database and Azure SQL Managed Instance)
• tune queries by using cache (Microsoft Documentation: Performance tuning with a result set caching)
• optimize pipelines for analytical or transactional purposes (Microsoft Documentation: What is Hyperspace?)
• optimize pipeline for descriptive versus analytical workloads (Microsoft Documentation: Optimize Apache Spark jobs in Azure Synapse Analytics)
• troubleshoot a failed spark job (Microsoft Documentation: Troubleshoot Apache Spark by using Azure HDInsight, Troubleshoot a slow or failing job on an HDInsight cluster)
• troubleshoot a failed pipeline run (Microsoft Documentation: Troubleshoot pipeline orchestration and triggers in Azure Data Factory)

For More: Check Exam DP-203: Data Engineering on Microsoft Azure FAQs

Exam Policies

Microsoft exam policies cover the exam-related details and information with providing the exam giving procedures. These exam policies consist of certain rules that have to be followed during exam time. However, some of the policies include:

Exam retake policy

• This states that candidates who will not be able to pass the exam for the first time must wait 24 hours before retaking the exam. During this time, they can go onto the certification dashboard and reschedule the exam. If this happens for the second time then, they have to wait for at least 14 days before retaking the exam. And, this 14-day waiting period is also inflicted between the third and fourth attempts and then, fourth and fifth attempts. However, candidates can only give any exam five times a year. 

Exam reschedule and the cancellation policy

• Microsoft temporarily waives the reschedule and cancellation fee if candidates cancel their exams within 24 hours before the scheduled appointment. However, for rescheduling or canceling an appointment there is no charge if it is executed at least 6 business days prior to your appointment. But, if a candidate cancels or reschedules an exam within 5 business days of your registered exam time then, a fee will be applied. 

Preparation Guide for Microsoft DP-203 Exam

1. Getting Familiar with Exam objectives

For having a better preparation, Microsoft DP-203 exam objectives can be very helpful. As this will help to get familiar with the topics provided for the DP-203 exam. Candidates can go through the sections and subsections to learn about the pattern of the exam. However, for the DP-203 exam, the topics include:

• Firstly, designing and implementing data storage
• Secondly, designing and developing data processing
• Next, design and implement data security 
• Lastly, monitoring and optimizing data storage and data processing

2. Microsoft Learning Platform

Microsoft offers learning platforms that cover various study resources to help candidates during exam preparation. For the DP-203 exam preparation, go through the Microsoft official website to get all the necessary information and the exam content outline. 

3. Microsoft Docs

Microsoft documentation refers to the source of knowledge that works as a reference for all the topics in the DP-203 exam. This provides detailed information about the exam concepts by covering different scales of different Azure services. Moreover, this consists of modules that will help you gain a lot of knowledge about concepts according to the exam.

4. Online Study Groups

Candidates can take advantage of the online study groups during the exam preparation. That is to say, joining the study groups will help you to stay connected with the professionals who are already on this pathway. Moreover, you can discuss your query or the issue related to the exam in this group and even take the DP-203 exam study notes.

5. Practice Tests

By using DP-203 exam practice tests, you will know about your weak and strong areas. Moreover, you will be able to enhance your answering skills for manning time management thus saving a lot of time during the exam. However, the good way to start taking the DP-203 exam practice tests is after completing a full topic and then try the mock tests for that. As a result, your revision will get stronger and you will get a better understanding. So, find the best DP-203 practice exam tests and crack the exam.

Start preparing for Data Engineering on Microsoft Azure DP-203 Exam Now!