DP-203: Data Engineering on Microsoft Azure Exam Prep 2023

Description

100% Cross after practising all these questions.

Newest Questions of 2023 examination included.

Candidates for this examination should have stable data of information processing languages, together with SQL, Python, and Scala, and they should perceive parallel processing and knowledge structure patterns. They need to be proficient in utilizing Azure Data Manufacturing facility, Azure Synapse Analytics, Azure Stream Analytics, Azure Occasion Hubs, Azure Data Lake Storage, and Azure Databricks to create knowledge processing options.

• Design and implement knowledge storage (15–20%)

• Develop knowledge processing (40–45%)

• Safe, monitor, and optimize knowledge storage and knowledge processing (30–35%)

Design and implement knowledge storage (15–20%)

Implement a partition technique

• Implement a partition technique for information

• Implement a partition technique for analytical workloads

• Implement a partition technique for streaming workloads

• Implement a partition technique for Azure Synapse Analytics

• Establish when partitioning is required in Azure Data Lake Storage Gen2

Design and implement the info exploration layer

• Create and execute queries through the use of a compute answer that leverages SQL serverless and Spark cluster

• Implement Azure Synapse Analytics database templates

• Suggest Azure Synapse Analytics database templates

• Push new or up to date knowledge lineage to Microsoft Purview

• Browse and search metadata in Microsoft Purview Data Catalog

Develop knowledge processing (40–45%)

Ingest and remodel knowledge

• Design and implement incremental masses

• Rework knowledge through the use of Apache Spark

• Rework knowledge through the use of Transact-SQL (T-SQL)

• Ingest and remodel knowledge through the use of Azure Synapse Pipelines or Azure Data Manufacturing facility

• Rework knowledge through the use of Azure Stream Analytics

• Cleanse knowledge

• Deal with duplicate knowledge

• Deal with lacking knowledge

• Deal with late-arriving knowledge

• Cut up knowledge

• Shred JSON

• Encode and decode knowledge

• Configure error dealing with for a metamorphosis

• Normalize and denormalize values

• Carry out knowledge exploratory evaluation

Develop a batch processing answer

• Develop batch processing options through the use of Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Manufacturing facility

• Use PolyBase to load knowledge to a SQL pool

• Implement Azure Synapse Hyperlink and question the replicated knowledge

• Create knowledge pipelines

• Scale sources

• Configure the batch dimension

• Create exams for knowledge pipelines

• Combine Jupyter or Python notebooks into a knowledge pipeline

• Upsert knowledge

• Revert knowledge to a earlier state

• Configure exception dealing with

• Configure batch retention

• Learn from and write to a delta lake

Develop a stream processing answer

• Create a stream processing answer through the use of Stream Analytics and Azure Occasion Hubs

• Course of knowledge through the use of Spark structured streaming

• Create windowed aggregates

• Deal with schema drift

• Course of time collection knowledge

• Course of knowledge throughout partitions

• Course of inside one partition

• Configure checkpoints and watermarking throughout processing

• Scale sources

• Create exams for knowledge pipelines

• Optimize pipelines for analytical or transactional functions

• Deal with interruptions

• Configure exception dealing with

• Upsert knowledge

• Replay archived stream knowledge

Handle batches and pipelines

• Set off batches

• Deal with failed batch masses

• Validate batch masses

• Handle knowledge pipelines in Azure Data Manufacturing facility or Azure Synapse Pipelines

• Schedule knowledge pipelines in Data Manufacturing facility or Azure Synapse Pipelines

• Implement model management for pipeline artifacts

• Handle Spark jobs in a pipeline

Safe, monitor, and optimize knowledge storage and knowledge processing (30–35%)

Implement knowledge safety

• Implement knowledge masking

• Encrypt knowledge at relaxation and in movement

• Implement row-level and column-level safety

• Implement Azure role-based entry management (RBAC)

• Implement POSIX-like entry management lists (ACLs) for Data Lake Storage Gen2

• Implement a knowledge retention coverage

• Implement safe endpoints (non-public and public)

• Implement useful resource tokens in Azure Databricks

• Load a DataFrame with delicate data

• Write encrypted knowledge to tables or Parquet information

• Handle delicate data

Monitor knowledge storage and knowledge processing

• Implement logging utilized by Azure Monitor

• Configure monitoring providers

• Monitor stream processing

• Measure efficiency of information motion

• Monitor and replace statistics about knowledge throughout a system

• Monitor knowledge pipeline efficiency

• Measure question efficiency

• Schedule and monitor pipeline exams

• Interpret Azure Monitor metrics and logs

• Implement a pipeline alert technique

Optimize and troubleshoot knowledge storage and knowledge processing

• Compact small information

• Deal with skew in knowledge

• Deal with knowledge spill

• Optimize useful resource administration

• Tune queries through the use of indexers

• Tune queries through the use of cache

• Troubleshoot a failed Spark job

• Troubleshoot a failed pipeline run, together with actions executed in exterior providers

Azure Data Engineers additionally assist be certain that knowledge pipelines and knowledge shops are high-performing, environment friendly, organized, and dependable, given a set of enterprise necessities and constraints. They take care of unanticipated points swiftly, and so they reduce knowledge loss. Additionally they design, implement, monitor, and optimize knowledge platforms to satisfy the info pipelines wants.

A candidate for this examination should have robust data of information processing languages reminiscent of SQL, Python, or Scala, and they should perceive parallel processing and knowledge structure patterns.

• Design and implement knowledge storage (40–45%)

• Design and develop knowledge processing (25–30%)

• Design and implement knowledge safety (10–15%)

• Monitor and optimize knowledge storage and knowledge processing (10–15%)

Design and implement knowledge storage (40–45%)

Design a knowledge storage construction

• Design an Azure Data Lake answer

• Suggest file sorts for storage

• Suggest file sorts for analytical queries

• Design for environment friendly querying

• Design for knowledge pruning

• Design a folder construction that represents the degrees of information transformation

• Design a distribution technique

• Design a knowledge archiving answer

Design a partition technique

• Design a partition technique for information

• Design a partition technique for analytical workloads

• Design a partition technique for effectivity/efficiency

• Design a partition technique for Azure Synapse Analytics

• Establish when partitioning is required in Azure Data Lake Storage Gen2

Design the serving layer

• Design star schemas

• Design slowly altering dimensions

• Design a dimensional hierarchy

• Design an answer for temporal knowledge

• Design for incremental loading

• Design analytical shops

• Design metastores in Azure Synapse Analytics and Azure Databricks

Implement bodily knowledge storage buildings

• Implement compression

• Implement partitioning

• Implement sharding

• Implement completely different desk geometries with Azure Synapse Analytics swimming pools

• Implement knowledge redundancy

• Implement distributions

• Implement knowledge archiving

Implement logical knowledge buildings

• Construct a temporal knowledge answer

• Construct a slowly altering dimension

• Construct a logical folder construction

• Construct exterior tables

• Implement file and folder buildings for environment friendly querying and knowledge pruning

Implement the serving layer

• Ship knowledge in a relational star

• Ship knowledge in Parquet information

• Keep metadata

• Implement a dimensional hierarchy

Design and develop knowledge processing (25–30%)

Ingest and remodel knowledge

• Rework knowledge through the use of Apache Spark

• Rework knowledge through the use of Transact-SQL

• Rework knowledge through the use of Data Manufacturing facility

• Rework knowledge through the use of Azure Synapse Pipelines

• Rework knowledge through the use of Stream Analytics

• Cleanse knowledge

• Cut up knowledge

• Shred JSON

• Encode and decode knowledge

• Configure error dealing with for the transformation

• Normalize and denormalize values

• Rework knowledge through the use of Scala

• Carry out knowledge exploratory evaluation

Design and develop a batch processing answer

• Develop batch processing options through the use of Data Manufacturing facility, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks

• Create knowledge pipelines

• Design and implement incremental knowledge masses

• Design and develop slowly altering dimensions

• Deal with safety and compliance necessities

• Scale sources

• Configure the batch dimension

• Design and create exams for knowledge pipelines

• Combine Jupyter/Python notebooks into a knowledge pipeline

• Deal with duplicate knowledge

• Deal with lacking knowledge

• Deal with late-arriving knowledge

• Upsert knowledge

• Regress to a earlier state

• Design and configure exception dealing with

• Configure batch retention

• Design a batch processing answer

• Debug Spark jobs through the use of the Spark UI

Design and develop a stream processing answer

• Develop a stream processing answer through the use of Stream Analytics, Azure Databricks, and Azure Occasion Hubs

• Course of knowledge through the use of Spark structured streaming

• Monitor for efficiency and purposeful regressions

• Design and create windowed aggregates

• Deal with schema drift

• Course of time collection knowledge

• Course of throughout partitions

• Course of inside one partition

• Configure checkpoints/watermarking throughout processing

• Scale sources

• Design and create exams for knowledge pipelines

• Optimize pipelines for analytical or transactional functions

• Deal with interruptions

• Design and configure exception dealing with

• Upsert knowledge

• Replay archived stream knowledge

• Design a stream processing answer

Handle batches and pipelines

• Set off batches

• Deal with failed batch masses

• Validate batch masses

• Handle knowledge pipelines in Data Manufacturing facility/Synapse Pipelines

• Schedule knowledge pipelines in Data Manufacturing facility/Synapse Pipelines

• Implement model management for pipeline artifacts

• Handle Spark jobs in a pipeline

Design and implement knowledge safety (10–15%)

Design safety for knowledge insurance policies and requirements

• Design knowledge encryption for knowledge at relaxation and in transit

• Design a knowledge auditing technique

• Design a knowledge masking technique

• Design for knowledge privateness

• Design a knowledge retention coverage

• Design to purge knowledge based mostly on enterprise necessities

• Design Azure role-based entry management (Azure RBAC) and POSIX-like Entry Management Listing (ACL) for Data Lake Storage Gen2

• Design row-level and column-level safety

Implement knowledge safety

• Implement knowledge masking

• Encrypt knowledge at relaxation and in movement

• Implement row-level and column-level safety

• Implement Azure RBAC

• Implement POSIX-like ACLs for Data Lake Storage Gen2

• Implement a knowledge retention coverage

• Implement a knowledge auditing technique

• Handle identities, keys, and secrets and techniques throughout completely different knowledge platform applied sciences

• Implement safe endpoints (non-public and public)

• Implement useful resource tokens in Azure Databricks

• Load a DataFrame with delicate data

• Write encrypted knowledge to tables or Parquet information

• Handle delicate data

Monitor and optimize knowledge storage and knowledge processing (10–15%)

Monitor knowledge storage and knowledge processing

• Implement logging utilized by Azure Monitor

• Configure monitoring providers

• Measure efficiency of information motion

• Monitor and replace statistics about knowledge throughout a system

• Monitor knowledge pipeline efficiency

• Measure question efficiency

• Monitor cluster efficiency

• Perceive customized logging choices

• Schedule and monitor pipeline exams

• Interpret Azure Monitor metrics and logs

• Interpret a Spark directed acyclic graph (DAG)

Optimize and troubleshoot knowledge storage and knowledge processing

• Compact small information

• Rewrite user-defined features (UDFs)

• Deal with skew in knowledge

• Deal with knowledge spill

• Tune shuffle partitions

• Discover shuffling in a pipeline

• Optimize useful resource administration

• Tune queries through the use of indexers

• Tune queries through the use of cache

• Optimize pipelines f

  
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