In the end purchasing DP-203 Korean test questions and dumps will be the best choice for your exam. We assure you 100% pass DP-203 Korean exam with our exam cram pdf file. No help Full Refund.
Data Engineering on Microsoft Azure Exam Certification Details:
| Passing Score | 700 / 1000 |
| Exam Code | DP-203 |
| Duration | 150 mins |
| Books / Training | DP-203T00: Data Engineering on Microsoft Azure |
| Number of Questions | 40-60 |
| Exam Price | $165 (USD) |
| Schedule Exam | Pearson VUE |
| Sample Questions | Data Engineering on Microsoft Azure Sample Questions |
| Exam Name | Microsoft Certified - Azure Data Engineer Associate |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
Some people wonder how they can improve themselves and get promotion; they feel their career is into a bottleneck. Yes it is time to study, pass exam and get the vital certification with DP-203 Korean test questions and dumps. Once there is a good opportunity you will have vital advantages and stand out. Why are DP-203 Korean test questions and dumps important? The reason is below:
1. The DP-203 Korean test exam is very difficult and the failure rate is quite high according to official statistics.
2. The DP-203 Korean test cost is high; if you fail you should try and pay twice or more.
3. Since you are a busy-working man you may have little time on systematic studying and preparation before the real DP-203 Korean test exam. You will feel nervous and stressful every day before you pass the DP-203 Korean test exam.
4. You will feel aimless while studying without DP-203 Korean exam cram sheet. You will waste more time and your efficiency will be low.
The DP-203 Korean test questions and dumps have three versions:
1. The exam cram pdf file is used to reading directly and printing out for DP-203 Korean practice.
2. The test exam soft version is used to download on computer to test online and DP-203 Korean exam simulation.
3. The test exam online version is used to download on all electronics including soft version's functions. It is interactive and interesting for DP-203 Korean studying.
Microsoft DP-203 Exam Syllabus Topics:
| Topic | Details |
|---|---|
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 |
So if you choose to buy DP-203 Korean test questions and dumps it is more efficient for you to pass the test exam. You just master and recite the test questions and dumps. It saves a lot of time and money. You will feel casual while DP-203 Korean test online by our soft.
So far we are the best DP-203 Korean test questions and dumps provider. We can guarantee you pass exam. If you fail the DP-203 Korean exam and we will full refund to you.
Before purchasing I advise you to download our free DP-203 Korean exam cram pdf. It is free for your reference. You enter your email address and download DP-203 Korean dumps, very easy. Also please rest assured that your information will be kept in secret and safe. We won't send you advertisement without your permission.
After purchasing you can download the complete DP-203 Korean test questions and dumps soon even in official holidays. We are 7*24 online service. Whenever you send emails to us we will reply you in two hours.
After passing test exam if you still want to get the latest version about DP-203 Korean test questions and dumps please provide your email address to us, we will send you once updated. We have one-year service warranty. If you do not provide us email address we will think you do not want to receive these emails and won't send you junk emails.
After passing test exam if you want to purchase other test exam questions and DP-203 Korean dumps we will give you discount. Or if you purchase for your company and want to build long-term relationship with us we will give you discount too. Please email us your thoughts. You will have priority to get our holiday sales coupe as one of our old customers.
