This certification is an asset for Data Engineers working with Azure or anyone looking to enhance their expertise with it. It's also an excellent way to showcase your knowledge of Azure's powerful data services and their applications in modern Data Lake environments using the cloud.
The DP-203 exam covers the most common Azure data-related tools:
- Storage Account and Azure Data Lake Storage
- Azure Data Factory
- Azure Synapse Analytics
- Azure SQL Server
- Azure EventHubs and Stream Analytics
- Azure Entra ID
- Azure Databricks
I strongly recommend having some prior experience with any cloud environment (AWS, GCP, or Azure) and its data-related services; familiarity with relational database management systems, such as MySQL or PostgreSQL, can give you an edge. Knowledge of Python and tools like Jupyter Notebook or Jupyter Lab, is highly beneficial.
Tip #1: Leverage Udemy Course(s)
While the official Microsoft Learning path is an excellent resource, it’s not your only option.
Platforms like Udemy offer comprehensive courses that cover the entire test scope. At DataArt, we have corporate access to Udemy Business, and I recommend the DP-203 - Data Engineering on Microsoft Azure course. It provides in-depth coverage of the exam topics and is a great starting point for your preparation.
Tip #2: Strengthen Your SQL Skills
SQL is a cornerstone of the exam and your career as a Data Engineer, testing your ability to write good queries, create objects, understand performance points, and suggest improvement points.
So, be prepared to complete SQL sentences with the proper keywords, answer what a determined query is doing, etc. To sharpen your skills, check out The Complete SQL Bootcamp: Go from Zero to Hero or carefully watch the dedicated section 3 in the recommended Udemy Azure course I’ve mentioned in Tip #1.
Pay special attention to Stream Analytics windowing functions, as many questions involve understanding the differences between Tumbling, Hopping, Sliding, Session, and Snapshot windows.
Tip #3: Dive into PySpark and Scala
As a Data Engineer, Python is essential, but mastering PySpark can take your skills to the next level. PySpark, the Python API for Apache Spark (an open-source distributed computing system), enables large-scale data analytics and processing by leveraging the power of Spark's distributed processing capabilities. To get started, learn Scala, the native language for Spark. Scala also serves as Spark's native API.
I recommend another Udemy course— Spark and Python for Big Data with PySpark. But, if you do not have enough time to dedicate to a full course, be sure to focus on section 7 of the Azure course: Design and Develop Data Processing – A look at Spark.
Tip #4: Use YouTube Questions for Exam Prep
YouTube is a goldmine for practical exam insights. I recommend the DP 203 - Real Questions | Answers | Explanation playlist by The Tech Blackboard.
But don't expect the same questions to appear! Use the playlist to understand the logic and prepare for similar challenges.
Tip #5: Understand the Context!
Context is king. It changes everything, including the exam answer. Azure services often have overlapping functionalities, so selecting the right tool depends on the scenario.
Here is an example: If a Data Lake has a data source from a stream, which Azure service should you use?
Answer: Azure Event Hub.
Why? Azure Event Hubs is a fully managed, real-time data ingestion and streaming service provided by Microsoft Azure. It allows users to capture, process, and analyze massive amounts of data from various sources in real time, making it ideal for scenarios involving event streaming, data logging, and telemetry from distributed systems like IoT devices, applications, and cloud infrastructure.
Let’s take a look at another question: Which resource you should turn on the Azure Storage to enable Azure Data Lake Storage?
Answer: Hierarchical Namespace.
Why? This setting transforms a standard Blob Storage account into an Azure Data Lake Storage Gen2 account, enabling file and directory-level organization and management, like traditional file systems.
Anyway, this tip will come in handy for all cloud environments you'll work with.
Conclusion
Prior experience with Azure is not necessary. However, keeping the following suggestions in mind, will significantly enhance your preparation:
- Don't expect the same questions; learn the concepts behind them.
- Keep the context in mind when answering.
- Beware of SQL, Python/PySpark, and Scala questions.
Stay focused, believe in yourself, and best of luck!
