The DP 203 Certification is the next level after the DP-200 Designing an Azure Data Solution and Implementing an Azure Data Solution certifications.
The article covers everything you should know before applying for DP-203 certification.
The topics covered in this blog are:
- DP-203 Certification Overview
- Who is Azure Data Engineer?
- Why You Should Learn Data Engineer?
- Who This Certification is for?
- Benefits of DP-203 Certification
- DP-203 Exam Details
- DP-203 Exam Skills Measured
- How to Register for Azure DP 203 Exam
- Pre-requisites for DP-203 Certification
- DP 203 Study Guide
- DP-203 Exam Retake Policy
- DP 203 Exam Day Tips
- Conclusion
- FAQs
DP-203 Certification Overview
The DP-203 is an advanced-level certification from Microsoft Azure for Data Engineer. After getting DP-203 certification, candidates get the credibility and validation for Azure Data Engineer skills such as Designing, implementing, processing, monitoring, optimizing data storage and security of Data.
With Azure Data Engineers, you can ensure that data pipelines and data stores are optimized for performance and reliability, based on your business needs and constraints. You will be able to design, implement, and monitor data platforms to meet the needs of data pipelines. Hence, once you understand and master this certification you can easily become a successful Azure Data Engineer.
Are you new to the Azure cloud? Do check out our blog post on the Microsoft Azure Certification Path 2023 and choose the best certification for you.
Boost your confidence for the DP-203: Data Engineering on Microsoft Azure Exam with our trusted practice test. Enroll Now and pave your way to certification!
Who is Azure Data Engineer?
Azure Data Engineers are responsible for clearly separating and differentiating raw data into structured data. They integrate, transform and consolidate data from unstructured data into structured data. And this structured data is used for building analytics solutions.
Moreover, they help the stakeholders to clearly understand the information through exploration. They construct efficiently and design particular instructions for processing pipelines with the help of specific tools and strategies. To produce better data for evaluation, they use Azure data offerings and languages.
Following are the responsibilities of an Azure Data Engineer:
- Developing and designing data processing and storage solutions for enterprises.
- Install, configure, and manage cloud-based data services, such as databases, blob services, and analytics.
- Securing the stored data and the platform, so that only necessary users have the access to the data.
- Monitoring the systems to make sure it is running properly and are cost-effective.
Why You Should Learn Data Engineer?
Data engineers are the people who understand and connect the raw data into structured data within a company. They accomplish this by doing,
- Accessing, collecting, auditing, and cleaning data from systems and converting it into useable data for enterprises.
- Maintaining the database
- Building pipelines
- Monitor and manage data systems
- Creating data scientists’ output in a scalable manner.
So, they are the front lines of data strategy, the first people to tackle the unstructured data and convert it into structured one. They are people on whose shoulders data analysts and data scientists stand.
For this reason, there is a huge demand for Data engineers in the IT sector, as they daily produce raw data.
As for some resources, the average salary for a Data Engineer is $116,591. It is also known that they make 261% more than the national average salary.
Who This Certification is For?
DP-203 certification is the ideal for the candidate,
- Who is interested in Data Engineering.
- For the professionals of Data architects, Data Administration and Business Intelligence.
- Candidates who know SQL, Python, Scala, or other data processing languages.
- Candidates who are good at parallel processing and data architecture patterns.
- Data Engineers who can transform and consolidate unstructured data into structured data.
Benefits of DP-203 Certification
- There is a huge demand for Data Engineers. In addition, Microsoft certification is globally recognized.
- After being DP-203 certified, 26% reported job promotions and 35% of technical professionals reported that certification led to salary or wage increments.
- DP-203 certification leads to rampant gain in jobs and earnings.
- The CV with Microsoft certification advances your job profile and increases the chances of getting chosen.
Check Out: ADF Interview Questions
DP-203 Exam Details
Exam Name DP-203: Data Engineering on Microsoft Azure | Exam Duration 180 Minutes |
Exam Type Multiple Choice Examination | Number of Questions 40 – 60 Questions |
Exam Fee $165 | Eligibility/Pre-Requisite None |
Exam validity 1 years | Exam Languages English, Japanese, Korean, and Simplified Chinese |
DP-203 Exam Skills Measured
Design and implement data storage | 40–45% |
Design and develop data processing | 25–30% |
Optimize data storage and data processing | 10-15% |
Design and implement data security | 10-15% |
How to Register for Azure DP 203 Exam
You can register for the Microsoft Azure Data Engineer Exam (DP-203) by going to the Official Microsoft Page.
Pre-requisites for DP-203 Certification
For this exam, candidates must have a decent knowledge of Data Processing Languages such as:
- SQL
- Python
- Scala
DP 203 Study Guide
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 meta stores 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
DP-203 Exam Retake Policy
The DP-203 exam retake policy is as follows:
- If a candidate fails on the first attempt, they must wait for 24 hours before retaking the exam.
- If a candidate again fails on the second attempt, then the candidate will have to wait for 14 days.
- A candidate will be given a maximum of five attempts to retake an exam in a year.
DP-203 Exam Day Tips
- With practice tests, you can become familiar with the test format while broadening your knowledge at the same time.
- Those questions are very similar to those that you will find on test day, and more importantly, each answer is explained with a reference to documentation.
- Let your common sense and previous knowledge take center stage in the first learning phase, and try to answer every question.
- At least a week before the exam, make sure you schedule it.
- A quiet space should be provided for test-taking.
- Do not read the question loudly otherwise, you may get disqualified
- Please remove all paper, pencils, external keyboards, etc. from sight before taking photos.
- During the test, be mindful of your eyes. Pearson VUE will monitor you throughout your test by using the front-facing camera on your device. You may be accused of cheating if you fail to pay attention to your computer screen during the test.
- Avoid staring into the distance while you are thinking during your exam.
Conclusion
The certification is for those candidates who want to demonstrate expertise in designing and implementing data solutions with the use of Microsoft Azure data services.
Through this certification, you will learn to integrate, transform and consolidate unstructured data into structured data, that are suitable for building analytical solutions for the company.
Make sure to understand the concept behind the answers and eventually you will be able to use this knowledge to pass every practice and actual DP-203 certification test.
I hope this article is helpful to you and wish you good luck!
If you have any questions, please feel free to ask in below comment section.
FAQs
Q1. Is DP 203 easy?
The DP-203 exam can be challenging for some candidates, but it is considered to be a moderate-level exam overall. The exam is designed to test your knowledge and skills in designing and implementing data storage solutions on the Azure platform.
Q2. Is DP 203 worth IT?
Yes, DP-203 certification is worth it for those who want to advance their career in the field of data engineering and demonstrate their proficiency in designing and implementing data storage solutions on the Azure platform.
Q3. What is the salary of Azure Data Engineer?
The salary of an Azure Data Engineer can vary depending on several factors such as location, industry, years of experience, and job role. However, according to data from various job search websites, the average salary for an Azure Data Engineer is around $130,000 per year in the United States.
Q4. How long is DP 203 certificate valid for?
DP-203: Data Engineering on Microsoft Azure Certification will remain valid for one year.
Q5. How long is the DP-203 exam?
DP-203 exam is 120 minutes long.
Q6. How many questions are there in the DP-203 exam?
The Data Engineering on Microsoft Azure Certification DP-203 exam has 40-60 questions.
Q7. What is the passing score for DP-203?
The passing score for the Microsoft DP203 exam is 700 out of 1000 marks.
Q8. How much does it cost to take the DP 203 exam?
The DP 203 exam costs $165 USD.
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