DP-203: Data Engineering Certification Practice Exam

Are you ready to take your data engineering skills on Microsoft Azure to the next level? Look no further than our comprehensive course: dp-203 Data Engineering on Microsoft Azure Exam. Designed specifically for professionals seeking a solid understanding of data engineering principles and techniques on Azure, this course is your key to success.

The dp-203 Data Engineering on Microsoft Azure Exam course is carefully crafted to ensure that you gain in-depth knowledge of data engineering concepts and their practical application in a Azure environment. With this course, you will attain the necessary skills and expertise to effectively design, implement, and monitor data solutions on Azure.

What sets our course apart is its focus on providing extensive practice tests with detailed explanations. As we firmly believe that practice makes perfect, we have incorporated a range of multiple-choice questions within the course to simulate the actual dp-203 exam. Solving these practice tests will not only familiarize you with the exam format, but it will also provide you with a hands-on experience and boost your confidence.

Each practice test has been meticulously curated to cover every aspect of data engineering on Microsoft Azure. You will be presented with a variety of scenarios and questions that require critical thinking and problem-solving skills. Our detailed explanations for each question will guide you through the reasoning behind the correct answer, allowing you to grasp the underlying concepts and solidify your understanding.

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)

• 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

• 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 storage 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

By enrolling in this course, you will gain access to exclusive practice test materials that are designed and updated to align with the current dp-203 exam syllabus. This ensures that you stay up-to-date with the latest industry trends and best practices in data engineering on Microsoft Azure.

Whether you are a seasoned data professional or just starting your journey in data engineering, this course is tailored to meet your needs. Our comprehensive curriculum covers a wide range of topics including data ingestion and transformation, data storage and processing, data monitoring and optimization, and much more.

Upon successful completion of this course, you will not only be well-prepared to pass the dp-203 exam with flying colors, but you will also be equipped with the skills necessary to tackle real-world data engineering challenges on Microsoft Azure. Don't miss this opportunity to enhance your data engineering expertise – enroll today in dp-203 Data Engineering on Microsoft Azure Exam.