DP-203: Data Engineering on Microsoft Azure Exam
Exam DP-203: Data Engineering on Microsoft Azure
About Exam DP-203: Data Engineering on Microsoft Azure
The candidates who are available for this exam should have the best subject matter expertise while get integrating, transforming, and consolidating data from several other structured as well as unstructured major data systems into the best relevant structure that is so much suitable, and authentic for building the best analytics solutions.
The Azure data engineers effectively help and support the stakeholders to easily understand the data through better exploration, and it also gets build-up and maintains the secured, authentic, and authentic compliant data processing pipelines through using various effective tools, and relevant techniques. These professionals also effectively use various Azure data services, and also relevant languages to store, and also produce the best cleansed, and enhanced best datasets for better, and authentic analysis.
A candidate who is going for pursuing this exam must have the strength, and effective knowledge for data processing best authentic bits of knowledge like Python, SQL, and SCALA programming languages, and parallelly the candidate is also required to understand the major pattern of architecture.
The Azure data engineers also ensure the user for the best data pipelines, and the relevant data stores which are high performing, more efficient, organized, and also reliable, and which effectively gives the required main set of the business constraints, and who effectively deals with the unanticipated major issues, and for this, they also tried to effectively minimize the loss of data source, and they also design, get implement, monitor, and effectively optimize the main platforms of data source for meeting the pipelines of the data source.
Overview ofDP-203: Data Engineering on Microsoft Azure Exam
Name of Exam: Data Engineering on Microsoft Azure
Exam Code: DP-203
Registration Price of Exam: $165 (USD)
Language of Exam: English
Passing score of Exam: The passing score of the exam is 700 marks out of 1000 Marks.
The number of questions in the Exam: The number of questions in the examination gets lie in between 40-60 questions.
Type of Exam: Cloud Computing
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Format of Exam: Multiple choice questions
Steps to study for the DP-203 Exam.
The major steps to study for the DP-2013 Exam are mainly as follows:
- Online courses: For an Online course, the candidate could effectively use the Udemy course to work through content as the online course showed the major tools in an action, which effectively contained the exam tips, and various best practice sets, and illustrated best concepts. The best official content is the mixture of both texts as well as videos with having more weight in the direction of tests whereas the Udemy courses have mainly the greater number of videos.
- Best authentic books: Any latest updated book would be the best for this exam, because the course content for the syllabus changes over time, and so for this, the books also are required to get outdated as quickly. For completing the course syllabus, the reading material would be a good option.
- Getting hands-on practices: Microsoft had its instructor-led authentic best training, but inspect of this, properly continuing with training in the labs for the related course would surely work through this.
Microsoft DP-203 Exam main topics
|
Exam section |
Weightage |
Objectives |
|
Designing, and implementing the storage of data |
40-45% |
Designing a data storage related effective structure Designing an Azure Data for the Lake solution Recommending the file types for best storage Designing an efficient querying Designing a data archiving authentic solution Designing a folder structure that mainly represents the main levels for the transformation of data. Designing the best distribution strategy. Designing a partitioning strategy Designing the best authentic partition strategy related to the files. Designing the partition strategy for the best analytical main workloads. Designing a partitioning strategy for the best efficient major performance Identifying the main region at when the partitioning is needed in the lake storage for Azure data in Gen2 Designing the serving layer Designing the relevant best star schemes. Designing a slowly best changing dimension. Designing a dimensional hierarchy. Designing the analytical stores. Designing the best solutions for the temporal data source. Designing the metastore in the Azure Synapse Analytics, and the Azure Databricks. |
|
Design, and develop the Processing of data |
25-30% |
Ingesting as well as get transforming the data Transforming the data as through using the Apache Spark. Transforming the data as through using Transact-SQL Transforming the data through using Stream analytics. Transforming the data through using the Pipelines of Azure Synapse. Split data, and shred JSON Encoding, and decoding data sources. Transforming data through using Scala. Managing batches, and Pipelines Triggered batches Handling the failed batch loads. Managing the pipelines for the data source in the Data Factory. Implementing the best version control for a pipeline that artifacts. Managing the Spark jobs in the pipeline. |
|
Designing, and implementing the data security |
10-15% |
Designing the data security for the major policies, and standards related to data. Designing the best encryption for data that is mainly in transit. Designing the auditing strategy for data. Designing a data masking-related strategy. Designing for the privacy of data. Design for purging the data that is mainly based on the requirements of the business. Implementing the data security Implementing the masking of the data source. Encrypting data either that is in motion, or at rest. Implementing the Azure RBAC. Effectively implementing the data retention policy. Managing the best identities, keys, and authentic secrets across several data platform's main technologies. Writing up the encrypted data to the tables, and the Parquet files. Managing sensitive authentic information. Loading the data frame with the best sensitive information. Implementing an authentic best strategy for auditing data. |
|
Monitoring, and optimizing the storage as well as the processing of data |
10-15% |
Monitor the storage of data, and the data processing Implementing the best logging that is used by Azure monitoring. Configure the best monitoring services Measuring the performances of movement of data. Interpreting the Spark that is directed as through acyclic graph. Interpreting Azure monitor main metrics, and logs. Effectively understanding the best options for custom logging. Get measuring the best query performance. Optimizing the better troubleshoot data storage, and the data processing Compacting all the smaller files. Rewriting up the user-defined best functions. Handling the data pill, and the skew in the data source. Tune queries through using the cache, and indexers. Optimizing the pipelines for the best descriptive versus bet analytical workloads. Finding shuffling in the pipeline. |
