Practice Exams Microsoft Azure DP-203 Data Engineering

Practice Exams | Microsoft Azure DP-203 Data Engineering

Be prepared for the Microsoft Azure Exam DP-203: Data Engineering on Microsoft Azure

上次更新 9/2021

英语

https://www.udemy.com/course/practice-exams-microsoft-azure-dp-203-data-engineering/

说明

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 Exam



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 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 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/IPython 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



The exam is available in the following languages: English



此课程面向哪些人：

Microsoft Azure professionals who want to be Microsoft DP-203 certified