최신 DP-203 무료덤프 - Microsoft Data Engineering on Microsoft Azure
You need to output files from Azure Data Factory.
Which file format should you use for each type of output? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Which file format should you use for each type of output? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
Box 1: Parquet
Parquet stores data in columns, while Avro stores data in a row-based format. By their very nature, column- oriented data stores are optimized for read-heavy analytical workloads, while row-based databases are best for write-heavy transactional workloads.
Box 2: Avro
An Avro schema is created using JSON format.
AVRO supports timestamps.
Note: Azure Data Factory supports the following file formats (not GZip or TXT).
Avro format
Binary format
Delimited text format
Excel format
JSON format
ORC format
Parquet format
XML format
Reference:
https://www.datanami.com/2018/05/16/big-data-file-formats-demystified
You have an Azure Synapse Analytics dedicated SQL pool.
You need to create a table named FactInternetSales that will be a large fact table in a dimensional model.
FactInternetSales will contain 100 million rows and two columns named SalesAmount and OrderQuantity.
Queries executed on FactInternetSales will aggregate the values in SalesAmount and OrderQuantity from the last year for a specific product. The solution must minimize the data size and query execution time.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to create a table named FactInternetSales that will be a large fact table in a dimensional model.
FactInternetSales will contain 100 million rows and two columns named SalesAmount and OrderQuantity.
Queries executed on FactInternetSales will aggregate the values in SalesAmount and OrderQuantity from the last year for a specific product. The solution must minimize the data size and query execution time.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
Box 1: (CLUSTERED COLUMNSTORE INDEX
CLUSTERED COLUMNSTORE INDEX
Columnstore indexes are the standard for storing and querying large data warehousing fact tables. This index uses column-based data storage and query processing to achieve gains up to 10 times the query performance in your data warehouse over traditional row-oriented storage. You can also achieve gains up to 10 times the data compression over the uncompressed data size. Beginning with SQL Server 2016 (13.x) SP1, columnstore indexes enable operational analytics: the ability to run performant real-time analytics on a transactional workload.
Note: Clustered columnstore index
A clustered columnstore index is the physical storage for the entire table.
To reduce fragmentation of the column segments and improve performance, the columnstore index might store some data temporarily into a clustered index called a deltastore and a B-tree list of IDs for deleted rows.
The deltastore operations are handled behind the scenes. To return the correct query results, the clustered columnstore index combines query results from both the columnstore and the deltastore.
Box 2: HASH([ProductKey])
A hash distributed table distributes rows based on the value in the distribution column. A hash distributed table is designed to achieve high performance for queries on large tables.
Choose a distribution column with data that distributes evenly
Reference: https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-overview
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- overview
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- distribute
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool named Poo 11 and a storage account. The storage account contains a blob container. The blob container contains multiple CSV files.
You plan to load the files into Pool! by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
You plan to load the files into Pool! by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays.
The data contains the following columns.
You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension.
To which table should you add each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
The data contains the following columns.
You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension.
To which table should you add each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
Box 1: DimEvent
Box 2: DimChannel
Box 3: FactEvents
Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc Reference:
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema
You have an Azure Databricks workspace and an Azure Data Lake Storage Gen2 account named storage!
New files are uploaded daily to storage1.
* Incrementally process new files as they are upkorage1 as a structured streaming source. The solution must meet the following requirements:
* Minimize implementation and maintenance effort.
* Minimize the cost of processing millions of files.
* Support schema inference and schema drift.
Which should you include in the recommendation?
New files are uploaded daily to storage1.
* Incrementally process new files as they are upkorage1 as a structured streaming source. The solution must meet the following requirements:
* Minimize implementation and maintenance effort.
* Minimize the cost of processing millions of files.
* Support schema inference and schema drift.
Which should you include in the recommendation?
정답: C
You have an Azure Data Factory pipeline that performs an incremental load of source data to an Azure Data Lake Storage Gen2 account.
Data to be loaded is identified by a column named LastUpdatedDate in the source table.
You plan to execute the pipeline every four hours.
You need to ensure that the pipeline execution meets the following requirements:
Automatically retries the execution when the pipeline run fails due to concurrency or throttling limits.
Supports backfilling existing data in the table.
Which type of trigger should you use?
Data to be loaded is identified by a column named LastUpdatedDate in the source table.
You plan to execute the pipeline every four hours.
You need to ensure that the pipeline execution meets the following requirements:
Automatically retries the execution when the pipeline run fails due to concurrency or throttling limits.
Supports backfilling existing data in the table.
Which type of trigger should you use?
정답: C
설명: (DumpTOP 회원만 볼 수 있음)
You have a data warehouse in Azure Synapse Analytics.
You need to ensure that the data in the data warehouse is encrypted at rest.
What should you enable?
You need to ensure that the data in the data warehouse is encrypted at rest.
What should you enable?
정답: B
설명: (DumpTOP 회원만 볼 수 있음)
You are designing a financial transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
TransactionType: 40 million rows per transaction type
CustomerSegment: 4 million per customer segment
TransactionMonth: 65 million rows per month
AccountType: 500 million per account type
You have the following query requirements:
Analysts will most commonly analyze transactions for a given month.
Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
TransactionType: 40 million rows per transaction type
CustomerSegment: 4 million per customer segment
TransactionMonth: 65 million rows per month
AccountType: 500 million per account type
You have the following query requirements:
Analysts will most commonly analyze transactions for a given month.
Transactions analysis will typically summarize transactions by transaction type, customer segment, and/or account type You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
정답: A
설명: (DumpTOP 회원만 볼 수 있음)
You are building an Azure Analytics query that will receive input data from Azure IoT Hub and write the results to Azure Blob storage.
You need to calculate the difference in readings per sensor per hour.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to calculate the difference in readings per sensor per hour.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
Box 1: LAG
The LAG analytic operator allows one to look up a "previous" event in an event stream, within certain constraints. It is very useful for computing the rate of growth of a variable, detecting when a variable crosses a threshold, or when a condition starts or stops being true.
Box 2: LIMIT DURATION
Example: Compute the rate of growth, per sensor:
SELECT sensorId,
growth = reading -
LAG(reading) OVER (PARTITION BY sensorId LIMIT DURATION(hour, 1))
FROM input
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/lag-azure-stream-analytics
Note: The 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 Data Lake Storage account that contains a staging zone.
You need to design a dairy process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.
Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes a mapping data low. and then inserts the data into the data warehouse.
Does this meet the goal?
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 Data Lake Storage account that contains a staging zone.
You need to design a dairy process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.
Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes a mapping data low. and then inserts the data into the data warehouse.
Does this meet the goal?
정답: A
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool named Pool1.
You have the queries shown in the following table.
You are evaluating whether to enable result set caching for Pool1. Which query results will be cached if result set caching is enabled?
You have the queries shown in the following table.
You are evaluating whether to enable result set caching for Pool1. Which query results will be cached if result set caching is enabled?
정답: C
You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytics dedicated SQL pool.
The CSV file contains columns named username, comment and date.
The data flow already contains the following:
* A source transformation
* A Derived Column transformation to set the appropriate types of data
* A sink transformation to land the data in the pool
You need to ensure that the data flow meets the following requirements;
* All valid rows must be written to the destination table.
* Truncation errors in the comment column must be avoided proactively.
* Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point
The CSV file contains columns named username, comment and date.
The data flow already contains the following:
* A source transformation
* A Derived Column transformation to set the appropriate types of data
* A sink transformation to land the data in the pool
You need to ensure that the data flow meets the following requirements;
* All valid rows must be written to the destination table.
* Truncation errors in the comment column must be avoided proactively.
* Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point
정답: A,D
A company uses the Azure Data Lake Storage Gen2 service.
You need to design a data archiving solution that meets the following requirements:
Data that is older than five years is accessed infrequency but must be available within one second when requested.
Data that is older than seven years in NOT accessed.
Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate option in he answers area.
NOTE: Each correct selection is worth one point.
You need to design a data archiving solution that meets the following requirements:
Data that is older than five years is accessed infrequency but must be available within one second when requested.
Data that is older than seven years in NOT accessed.
Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate option in he answers area.
NOTE: Each correct selection is worth one point.
정답:
Explanation: