Designing Microsoft Azure Data Solution topics are deeply explained in DP-201 exam dumps of DumpsSchool. The DP-201 exam is necessary to get the Microsoft Azure AI Engineer Associate Certification and by using DP-201 exam dumps candidates acquire useful knowledge to get this certification.
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Question No. 1
A company manufactures automobile parts. The company installs IoT sensors on manufacturing machinery.
You must design a solution that analyzes data from the sensors.
You need to recommend a solution that meets the following requirements:
Data must be analyzed in real-time.
Data queries must be deployed using continuous integration.
Data must be visualized by using charts and graphs.
Data must be available for ETL operations in the future.
The solution must support high-volume data ingestion.
Which three actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Question No. 2
You design data engineering solutions for a company.
A project requires analytics and visualization of large set of data. The project has the following requirements:
* Notebook scheduling
* Cluster automation
* Power BI Visualization
You need to recommend the appropriate Azure service.
Which Azure service should you recommend?
A databrick job is a way of running a notebook or JAR either immediately or on a scheduled basis.
Azure Databricks has two types of clusters: interactive and job. Interactive clusters are used to analyze data collaboratively with interactive notebooks. Job clusters are used to run fast and robust automated workloads using the UI or API.
You can visualize Data with Azure Databricks and Power BI Desktop.
Question No. 3
You are designing a big data storage solution. The solution must meet the following requirements:
* Provide unlimited account sizes.
* Support a hierarchical file system.
* Be optimized for parallel analytics workloads.
Which storage solution should you use?
Azure Data Lake Storage is optimized performance for parallel analytics workloads
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object
storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized.
Question No. 4
You need to recommend an Azure SQL Database service tier.
What should you recommend?
The data engineers must set the SQL Data Warehouse compute resources to consume 300 DWUs.
Note: There are three architectural models that are used in Azure SQL Database:
* General Purpose/Standard
* Business Critical/Premium
A: Business Critical service tier is designed for the applications that require low-latency responses from the underlying SSD storage (1-2 ms in average), fast recovery if the underlying infrastructure fails, or need to off-load reports, analytics, and read-only queries to the free of charge readable secondary replica of the primary database.
Question No. 5
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure SQL database that has columns. The columns contain sensitive Personally Identifiable Information (PII) data.
You need to design a solution that tracks and stores all the queries executed against the PII data. You must be able to review the data in Azure Monitor, and the data must be available for at least 45 days.
Solution: You execute a daily stored procedure that retrieves queries from Query Store, looks up the column classifications, and stores the results in a new table in the database.
Does this meet the goal?
Instead add classifications to the columns that contain sensitive data and turn on Auditing.
Note: Auditing has been enhanced to log sensitivity classifications or labels of the actual data that were returned by the query. This would enable you to gain insights on who is accessing sensitive data.
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