A Step-by-Step Guide to become Azure Data Engineer in 2024

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Azure Data Engineer

Have you decided to become a data engineer? Secondly, are you willing to earn the expertise on Microsoft Azure? But you are baffled by various queries like which is the best data engineering credential, how to prepare for it, what is its eligibility and what will be your career and after taking the credential and much more. Starting with your first query, which is the best credential for the data engineers, Microsoft Azure Data Engineer Associate DP-203 credential is on the top of the list. Now let’s delve deeper into the various aspects of this credential.

What is the DP-203 exam?

The Azure Data Engineering certification is a credential offered by Microsoft that validates a professional’s expertise in designing and implementing data solutions using Azure services. It covers various aspects such as data storage, data processing, and data security on the Azure platform.

What type of questions are expected in the DP-203 exam?

The type of questions in DP-203 exam are multiple-choice questions, single answer questions, or drop type of questions, arranged in the correct sequence type questions, scenario-based.

What is the duration of the DP-203 exam?

The time duration of the exam is 2 hours (120 minutes).

What is the cost of the DP-203?

The cost of the DP-203 exam is $165.

What languages are offered in the DP-203 exam?

The exam is offered in English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia).

What is the passing score of the exam?

The passing score of the exam is 700. A minimum of 70% marks are required to crack the exam.

Do I need to take the DP-200 and DP-203 exam?

According to the updated policies of the Microsoft Azure, you are not required to take the DP-200 and DP-201 exam. There are no formal prerequisites to take the DP-203 exam. If you have the required knowledge and skills, you can take the Azure data engineer associate exam directly.

DP-203 Exam

What is the updated course outline of the DP-600 exam?

Design and implement data storage (15–20%)

Implement a partition strategy

  • Implement a partition strategy for files
  • Implement a partition strategy for analytical workloads
  • Implement a partition strategy for streaming workloads
  • Implement a partition strategy for Azure Synapse Analytics
  • Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design and implement the data exploration layer

  • Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
  • Recommend and implement Azure Synapse Analytics database templates
  • Push new or updated data lineage to Microsoft Purview
  • Browse and search metadata in Microsoft Purview Data Catalog

Develop data processing (40–45%)

Ingest and transform data

  • Design and implement incremental loads
  • Transform data by using Apache Spark
  • Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics
  • Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
  • Transform data by using Azure Stream Analytics
  • Cleanse data
  • Handle duplicate data
  • Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery
  • Handle missing data
  • Handle late-arriving data
  • Split data
  • Shred JSON
  • Encode and decode data
  • Configure error handling for a transformation
  • Normalize and denormalize data
  • Perform data exploratory analysis

Develop a batch processing solution

  • Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
  • Use PolyBase to load data to a SQL pool
  • Implement Azure Synapse Link and query the replicated data
  • Create data pipelines
  • Scale resources
  • Configure the batch size
  • Create tests for data pipelines
  • Integrate Jupyter or Python notebooks into a data pipeline
  • Upsert data
  • Revert data to a previous state
  • Configure exception handling
  • Configure batch retention
  • Read from and write to a delta lake

Develop a stream processing solution

  • Create a stream processing solution by using Stream Analytics and Azure Event Hubs
  • Process data by using Spark structured streaming
  • Create windowed aggregates
  • Handle schema drift
  • Process time series data
  • Process data across partitions

Process within one partition

  • Configure checkpoints and watermarking during processing
  • Scale resources
  • Create tests for data pipelines
  • Optimize pipelines for analytical or transactional purposes
  • Handle interruptions
  • Configure exception handling
  • Upsert data
  • Replay archived stream data

Manage batches and pipelines

  • Trigger batches
  • Handle failed batch loads
  • Validate batch loads
  • Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
  • Schedule data pipelines in Data Factory or Azure Synapse Pipelines
  • Implement version control for pipeline artifacts
  • Manage Spark jobs in a pipeline

Secure, monitor, and optimize data sorage and data processing (30–35%)

Implement data security

  • Implement data masking
  • Encrypt data at rest and in motion
  • Implement row-level and column-level security
  • Implement Azure role-based access control (RBAC)
  • Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
  • Implement a data retention policy
  • 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 data storage and data processing

  • Implement logging used by Azure Monitor
  • Configure monitoring services
  • Monitor stream processing
  • Measure performance of data movement
  • Monitor and update statistics about data across a system
  • Monitor data pipeline performance
  • Measure query performance
  • Schedule and monitor pipeline tests
  • Interpret Azure Monitor metrics and logs
  • Implement a pipeline alert strategy

Optimize and troubleshoot data storage and data processing

  • Compact small files
  • Handle skew in data
  • Handle data spill
  • Optimize resource management
  • Tune queries by using indexers
  • Tune queries by using cache
  • Troubleshoot a failed Spark job
  • Troubleshoot a failed pipeline run, including activities executed in external services

Read more:- Updated DP-203  Exam Guide

Effective Preparation

Effective Preparation Strategy to Pass DP-203 in A Single Go

Preparing for the Azure Data Engineering exam (DP-203) requires a structured approach and a combination of study resources, hands-on practice, and strategic planning. Here’s a step-by-step guide to help you prepare effectively:

1. Assess Your Current Knowledge

Evaluate your existing knowledge and experience in Azure data engineering. Identify areas where you need to strengthen your skills. Understand the exam objectives outlined by Microsoft. Review the skills measured section to understand what topics will be covered in the exam. Refer to the official Microsoft documentation for Azure services and technologies covered in the exam. Study Azure documentation thoroughly to understand concepts, features, and best practices.

2. Take Advantage of Learning Paths and Online Courses

Develop a study plan that aligns with your schedule and allows ample time for preparation. Break down the exam topics into manageable sections and allocate study time accordingly. Enroll in Microsoft Learn’s learning paths specifically designed for Azure Data Engineering certification. Explore online courses offered by reputable platforms like Pluralsight, Coursera, or Udemy, focusing on Azure data engineering topics.

3. Learn the Practical Demonstration

Set up a free Azure account or utilize Azure free services to gain hands-on experience with Azure data services. Practice implementing data solutions, creating data pipelines, and performing data analytics using Azure services like Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. Work on real-world scenarios and projects to reinforce your understanding of Azure data engineering concepts.

4. Learn the required languages

You must acquire the required languages to prepare well for the exam. You must have the a strong grip on the following languages:

5. Practice intensely

Access practice exams and sample questions to assess your readiness for the actual exam. Analyze your performance in practice exams to identify areas where you need to focus more attention. The practice questions will help to clarify your concepts and get the

6. Review and reinforce your concepts

Review your study materials regularly to reinforce your understanding of key concepts. Focus on areas where you feel less confident and spend additional time strengthening your knowledge.

You need to understand parallel processing and data architecture patterns. You should be proficient in using the following to create data processing solutions:


More Information:-Unveiling the Significance of Microsoft DP-203 Exam in 2024

Worth of Data Engineer Credential for Individuals and Organizations

The importance of the Data Engineering credential, especially in the context of Azure, extends to both individuals and organizations, offering significant benefits to both parties:

For Individuals

  1. Obtaining a Data Engineering credential such as the Azure Data Engineer Associate certification demonstrates expertise and proficiency in designing and implementing data solutions on the Azure platform. It enhances your credibility and opens up new career opportunities in the rapidly growing field of cloud-based data engineering.
  2. With the increasing adoption of cloud technologies, organizations are actively seeking skilled professionals who can leverage Azure services for managing and processing data. Holding a Data Engineering credential validates your skills and makes you more desirable to employers.
  3. Certified data engineers often command higher salaries compared to non-certified professionals. The credential signifies your ability to work effectively with Azure data services, which can translate into better compensation packages and career growth opportunities.
  4. Earning a Data Engineering credential requires passing a rigorous exam that tests your knowledge and proficiency in Azure data services. It serves as tangible proof of your skills and expertise in data engineering, providing validation to potential employers and peers.

For Organizations

  1. Certification programs like Azure Data Engineering enable organizations to identify and recruit skilled professionals who possess the necessary expertise to design, implement, and manage data solutions on Azure. This ensures that the organization has access to a talent pool capable of driving its data initiatives forward.
  2. Certified data engineers have the knowledge and skills to architect robust data solutions on Azure, including data ingestion, storage, processing, and analytics. By leveraging certified talent, organizations can optimize their data management processes, leading to improved data quality, efficiency, and reliability.
  3. Certification programs serve as a catalyst for cloud adoption within organizations. By investing in training and certifying their workforce, organizations can expedite their migration to the cloud and maximize the benefits of Azure data services, such as scalability, flexibility, and cost-effectiveness.
  4. In today’s competitive landscape, organizations must leverage data effectively to gain insights, drive innovation, and maintain a competitive edge. Employing certified data engineers allows organizations to stay ahead of the curve by leveraging the full potential of Azure data services to derive actionable insights and make informed decisions.

Read more:- How to Crack Microsoft Certification

Jobs You Get Hired On After Obtaining DP-203 Credential

Here are six promising job roles that specifically align with the skills and expertise of Azure Data Engineers:

1. Azure Data Engineer

As the name suggests, this role focuses on designing, implementing, and managing data solutions on the Azure platform. Azure Data Engineers work with various Azure services such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure SQL Database to build scalable and efficient data pipelines, perform data ingestion, transformation, and storage, and ensure data security and compliance.

2. Cloud Data Architect

Cloud Data Architects are responsible for designing and implementing data architectures on cloud platforms like Azure. They work closely with stakeholders to understand business requirements, design data models, select appropriate Azure services, and ensure the scalability, reliability, and performance of data solutions. Azure Data Engineers with a strong understanding of cloud architecture principles and Azure data services are well-suited for this role.

3. Azure Big Data Engineer

Azure Big Data Engineers specialize in managing and processing large volumes of data using distributed computing technologies on the Azure platform. They design and implement big data solutions using services like Azure HDInsight, Azure Databricks, Azure Data Lake Storage, and Azure Cosmos DB. Azure Data Engineers with expertise in big data technologies and experience in building scalable data processing pipelines are ideal candidates for this role.

4. Azure Data Warehouse Developer

Azure Data Warehouse Developers focus on designing, building, and optimizing data warehouse solutions on Azure. They leverage Azure Synapse Analytics (formerly Azure SQL Data Warehouse) to create data warehouses that support advanced analytics, reporting, and business intelligence initiatives. Azure Data Engineers with proficiency in SQL, data modeling, and experience in implementing data warehouses on Azure are well-suited for this role.

5. Azure Machine Learning Engineer

Azure Machine Learning Engineers specialize in developing and deploying machine learning models and solutions on the Azure platform. They work with Azure Machine Learning service to build, train, and deploy models for various use cases such as predictive analytics, recommendation systems, and image recognition. Azure Data Engineers with a background in data science, machine learning, and experience in using Azure Machine Learning service can transition into this role.

6. Azure DevOps Engineer (Data Focus)

Azure DevOps Engineers with a focus on data specialize in implementing continuous integration, continuous delivery, and automation pipelines for data-related workloads on Azure. They leverage Azure DevOps tools and services to automate deployment processes, manage infrastructure as code, and ensure the reliability and scalability of data solutions. Azure Data Engineers with experience in DevOps practices, infrastructure automation, and proficiency in Azure DevOps tools can excel in this role.

Practice AZ-400 Exam  Dumps to get your certification 

Is It Hard to Obtain DP-203 Credential?

The difficulty of the Azure Data Engineer exam (DP-203) can vary depending on your level of experience, familiarity with Azure services, and preparation efforts. Here are some factors to consider when assessing the difficulty of the exam:

Challenge 1: Obtaining intense knowledge

The Azure Data Engineer Associate certification exam assumes a certain level of knowledge and experience with Azure data services, including Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Cosmos DB, and Azure HDInsight. If you’re already familiar with these services and have hands-on experience using them, you may find the exam less challenging.

Challenge 2: Learn to apply theoretical knowledge

Hands-on experience with Azure data services is crucial for success in the exam. The ability to apply theoretical knowledge to real-world scenarios and tasks is essential. If you have limited hands-on experience with Azure data services, you may find it more challenging to answer practical questions or scenario-based questions.

Challenge 3: Effective preparation strategy

Adequate preparation is key to passing the Azure Data Engineer exam. Depending on your learning style and study habits, you may need to invest a significant amount of time and effort in studying exam topics, reviewing documentation, taking practice exams, and completing hands-on labs or projects. Effective preparation can help you feel more confident and perform better on the exam.

Challenge 4: Solve the exam in a given time

The Azure Data Engineer exam is timed, and you’ll need to manage your time effectively to answer all questions within the allotted time frame. Some questions may require more time to analyze or solve than others, so it’s essential to budget your time wisely and pace yourself throughout the exam.

Final Remarks

In short, the DP-203 exam can enhance your career prospects and help you attain the required skills to ace in your professional life. The exam is a good fit for you if you have acquired the basic data skills and now you are in your mid-career hoping to get strong support. The exam is basically a professional credential so it is advisable for the candidates who have obtained a few years of data experience. If you are completely new to the data field, then hold on and pay attention to learn the required skills and languages to take the exam. There is a certain great scope of data engineers throughout the world. They are employed in various sectors like health, education, IT etc. If you are willing to take the Azure Data Engineer Associate exam, you must register for the exam.

Yes, anyone can take the Azure Data Engineer exam. Microsoft’s Azure certifications are open to all individuals who are interested in validating their skills and expertise in Azure technologies, including data engineering. However, it’s recommended that candidates have some level of experience and knowledge in data engineering concepts and Azure services before attempting the exam to increase their chances of success.

The time it takes to prepare for the Azure Data Engineer exam can vary depending on several factors, including your current level of experience, familiarity with Azure technologies, and the amount of time you can dedicate to studying each day. On average, candidates typically spend anywhere from 1 to 3 months preparing for the exam.

If you’re already familiar with Azure services and data engineering concepts, you might need less time to prepare, whereas those who are new to these topics may require more time for studying and practice. It’s essential to create a study plan, utilize relevant resources such as Microsoft’s official exam guide and practice tests, and allocate sufficient time each day to review and reinforce your knowledge.

Additionally, hands-on experience with Azure services and real-world data engineering projects can significantly enhance your preparation and readiness for the exam. Ultimately, the key is to tailor your study approach to your individual learning style and pace to ensure thorough preparation before taking the Azure Data Engineer exam.

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