DP-203認證考試解析 - DP-203考題套裝

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>> DP-203認證考試解析 <<

DP-203考題套裝 & DP-203試題

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最新的 Microsoft Certified: Azure Data Engineer Associate DP-203 免費考試真題 (Q254-Q259):

問題 #254
You have an Azure Synapse Analytics pipeline named pipeline1 that has concurrency set to 1.
To run pipeline 1, you create a new trigger as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the [graphic.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:

問題 #255
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model Pool1 will contain the following tables.

答案：

解題說明：

Explanation

問題 #256
You have an Azure Synapse Analytics SQL pool named Pool1 on a logical Microsoft SQL server named Server1.
You need to implement Transparent Data Encryption (TDE) on Pool1 by using a custom key named key1.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

答案：

解題說明：

Explanation:
Graphical user interface, text, application Description automatically generated

Step 1: Assign a managed identity to Server1
You will need an existing Managed Instance as a prerequisite.
Step 2: Create an Azure key vault and grant the managed identity permissions to the vault Create Resource and setup Azure Key Vault.
Step 3: Add key1 to the Azure key vault
The recommended way is to import an existing key from a .pfx file or get an existing key from the vault.
Alternatively, generate a new key directly in Azure Key Vault.
Step 4: Configure key1 as the TDE protector for Server1
Provide TDE Protector key
Step 5: Enable TDE on Pool1
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/managed-instance/scripts/transparent-data-encryption-byok-pow

問題 #257
You are designing 2 solution that will use tables in Delta Lake on Azure Databricks.
You need to minimize how long it takes to perform the following:
*Queries against non-partitioned tables
* Joins on non-partitioned columns
Which two options should you include in the solution? Each correct answer presents part of the solution.
(Choose Correct Answer and Give Explanation and References to Support the answers based from Data Engineering on Microsoft Azure)

A. Apache Spark caching
B. Z-Ordering
C. dynamic file pruning (DFP)
D. the clone command

答案：B,C

解題說明：
According to the information I found on the web, two options that you should include in the solution to minimize how long it takes to perform queries and joins on non-partitioned tables are:
* Z-Ordering: This is a technique to colocate related information in the same set of files. This co-locality is automatically used by Delta Lake in data-skipping algorithms. This behavior dramatically reduces the amount of data that Delta Lake on Azure Databricks needs to read123.
* Apache Spark caching: This is a feature that allows you to cache data in memory or on disk for faster access. Caching can improve the performance of repeated queries and joins on the same data. You can cache Delta tables using the CACHE TABLE or CACHE LAZY commands.
To minimize the time it takes to perform queries against non-partitioned tables and joins on non-partitioned columns in Delta Lake on Azure Databricks, the following options should be included in the solution:
A: Z-Ordering: Z-Ordering improves query performance by co-locating data that share the same column values in the same physical partitions. This reduces the need for shuffling data across nodes during query execution. By using Z-Ordering, you can avoid full table scans and reduce the amount of data processed.
B: Apache Spark caching: Caching data in memory can improve query performance by reducing the amount of data read from disk. This helps to speed up subsequent queries that need to access the same data. When you cache a table, the data is read from the data source and stored in memory. Subsequent queries can then read the data from memory, which is much faster than reading it from disk.
References:
* Delta Lake on Databricks: https://docs.databricks.com/delta/index.html
* Best Practices for Delta Lake on
Databricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta-lake-on-databricks.html

問題 #258
You are building an Azure Stream Analytics job to identify how much time a user spends interacting with a feature on a webpage.
The job receives events based on user actions on the webpage. Each row of data represents an event. Each event has a type of either 'start' or 'end'.
You need to calculate the duration between start and end events.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-stream-analytics-query-patterns

問題 #259
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