100% Pass DP-203 - Data Engineering on Microsoft Azure High Hit-Rate Actual Test Answers

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q273-Q278):

NEW QUESTION # 273
You are deploying a lake database by using an Azure Synapse database template.
You need to add additional tables to the database. The solution must use the same grouping method as the template tables.
'Which grouping method should you use?

A. partition style
B. size
C. business area
D. facts and dimensions

Answer: C

Explanation:
* Business area: This is how the Azure Synapse database templates group tables by default. Each template consists of one or more enterprise templates that contain tables grouped by business areas. For example, the Retail template has business areas such as Customer, Product, Sales, and Store123. Using the same
* grouping method as the template tables can help you maintain consistency and compatibility with the industry-specific data model.
https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/database-templates-in-azure-synapse-anal

NEW QUESTION # 274
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.

All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

NEW QUESTION # 275
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 Analytic dedicated SQL pool. The CSV file contains three 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.

A. Add a select transformation to select only the rows that will cause truncation errors.
B. To the data flow, add a sink transformation to write the rows to a file in blob storage.
C. To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.
D. To the data flow, add a filter transformation to filter out rows that will cause truncation errors.

Answer: B,C

Explanation:
B: Example:
1. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.

2. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.
A:
3. Now we need to log the rows that failed. Add a sink transformation to the BadRows stream for logging. Here, we'll "auto-map" all of the fields so that we have logging of the complete transaction record. This is a text-delimited CSV file output to a single file in Blob Storage. We'll call the log file "badrows.csv".

4. The completed data flow is shown below. We are now able to split off error rows to avoid the SQL truncation errors and put those entries into a log file. Meanwhile, successful rows can continue to write to our target database.

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-data-flow-error-rows

NEW QUESTION # 276
You are designing a real-time dashboard solution that will visualize streaming data from remote sensors that connect to the internet. The streaming data must be aggregated to show the average value of each 10-second interval. The data will be discarded after being displayed in the dashboard.
The solution will use Azure Stream Analytics and must meet the following requirements:
Minimize latency from an Azure Event hub to the dashboard.
Minimize the required storage.
Minimize development effort.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-power-bi-dashboard

NEW QUESTION # 277
You have a Microsoft SQL Server database that uses a third normal form schema.
You plan to migrate the data in the database to a star schema in an Azure Synapse Analytics dedicated SQI pool.
You need to design the dimension tables. The solution must optimize read operations.
What should you include in the solution? to answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation
Text, table Description automatically generated

Box 1: Denormalize to a second normal form
Denormalization is the process of transforming higher normal forms to lower normal forms via storing the join of higher normal form relations as a base relation. Denormalization increases the performance in data retrieval at cost of bringing update anomalies to a database.
Box 2: New identity columns
The collapsing relations strategy can be used in this step to collapse classification entities into component entities to obtain at dimension tables with single-part keys that connect directly to the fact table. The single-part key is a surrogate key generated to ensure it remains unique over time.
Example:
Diagram Description automatically generated

Note: A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.
Reference:
https://www.mssqltips.com/sqlservertip/5614/explore-the-role-of-normal-forms-in-dimensional-modeling/
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-identity

NEW QUESTION # 278
......

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100% Pass DP-203 - Data Engineering on Microsoft Azure High Hit-Rate Actual Test Answers

Pass DP-203 Exam with Updated DP-203 Actual Test Answers by RealVCE

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q273-Q278):

DP-203 Actual Test Answers | Valid DP-203 Actual Dump: Data Engineering on Microsoft Azure

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