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DP-203 practice dumps questions and answers

  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Updated: 2024-02-15
  • Q&A: 247 Questions and Answers
  • PDF Price: $36.99

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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/Python 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

DP-203 dumps, about Redundancy in the primary region questions - 2021-10-25

DP-203 dumps, about Redundancy in the primary region questions

Azure Storage DP-203 dumps, Can you do this question? - 2021-10-25

Azure Storage DP-203 dumps, Can you do this question?
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