Practice Exams Microsoft Azure DP-203 Data Engineering

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them. The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time. Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions. The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item “B” last time you went through the test. Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions. Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. Azure data engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns. Skills measured on Microsoft Azure DP-203 ExamDesign and Implement Data Storage (40-45%)Design and implement data storage (4045%)Design and develop data processing (2530%)Design and implement data security (1015%)Monitor and optimize data storage and data processing (1015%)The exam measures your ability to accomplish the following technical tasks: design and implement data storage; design and develop data processing; design and implement data security; and monitor and optimize data storage and data processing. Functional groupsDesign and implement data storage (4045%)Design a data storage structureDesign 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 metastores 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 archivingImplement 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 Deliver data in Parquet files Maintain metadata Implement a dimensional hierarchy Design and develop data processing (2530%) 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 pipelinesIntegrate Jupyter/Python notebooks in