MICROSOFT DP-203 EXAM | LATEST DP-203 EXAM REVIEW - BRING YOU THE BEST EXAM DP-203 PREPARATION

Microsoft DP-203 Exam | Latest DP-203 Exam Review - Bring you The Best Exam DP-203 Preparation

Microsoft DP-203 Exam | Latest DP-203 Exam Review - Bring you The Best Exam DP-203 Preparation

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How someone with a Microsoft DP-203 certificate will be better off?

There is no doubt that the DP-203 certificate on Microsoft Azure will be helpful in showing future employers and clients that you have a good understanding of the Microsoft Azure platform and have a sound knowledge of data management, data processing, and business intelligence. You can use this DP-203 certification to demonstrate your ability to build an enterprise-class data warehousing solution using Microsoft Azure's fully managed services. Microsoft DP-203 Dumps is the best way to ensure that you pass the exam on the first attempt. With these Microsoft DP-203 Practice Tests, you will be able to test your preparation before the real exam. After completing this course, you will be able to: Describe the challenges for data warehousing in the cloud. Understand how cloud storage works with Azure SQL Data Warehouse. Implement a relational database in the cloud using Azure SQL Database Managed Instance. Deploy a highly available and scalable data warehouse using Azure SQL Data Warehouse. External workloads load efficient nodes repartitioning folder selection guides duplicate hierarchy. Loading, archiving, pruning, premises, tabular, defined dimensional purposes. Stream table pipelines distribution handling control region temporal incremental dimensions structure tool. Demo PDF is also available.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q202-Q207):

NEW QUESTION # 202
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?

  • A. sliding
  • B. tumbling
  • C. hopping
  • D. snapshot

Answer: B

Explanation:
Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

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


NEW QUESTION # 203
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.
You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:
* Automatically scale down workers when the cluster is underutilized for three minutes.
* Minimize the time it takes to scale to the maximum number of workers.
* Minimize costs.
What should you do first?

  • A. Upgrade workspace1 to the Premium pricing tier.
  • B. Enable container services for workspace1.
  • C. Create a cluster policy in workspace1.
  • D. Set Cluster Mode to High Concurrency.

Answer: A

Explanation:
For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:
Scales up from min to max in 2 steps.
Can scale down even if the cluster is not idle by looking at shuffle file state.
Scales down based on a percentage of current nodes.
On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.
Note: Standard autoscaling
Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max.
You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.
Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes.
Scales down exponentially, starting with 1 node.
Reference:
https://docs.databricks.com/clusters/configure.html


NEW QUESTION # 204
You have an Azure subscription that contains an Azure Data Lake Storage Gen2 account named storage1.
Storage1 contains a container named container1. Container1 contains a directory named directory1.
Directory1 contains a file named file1.
You have an Azure Active Directory (Azure AD) user named User1 that is assigned the Storage Blob Data Reader role for storage1.
You need to ensure that User1 can append data to file1. The solution must use the principle of least privilege.
Which permissions should you grant? To answer, drag the appropriate permissions to the correct resources.
Each permission may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:

Explanation:

Explanation:
Box 1: Execute
If you are granting permissions by using only ACLs (no Azure RBAC), then to grant a security principal read or write access to a file, you'll need to give the security principal Execute permissions to the root folder of the container, and to each folder in the hierarchy of folders that lead to the file.
Box 2: Execute
On Directory: Execute (X): Required to traverse the child items of a directory Box 3: Write On file: Write (W): Can write or append to a file.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control


NEW QUESTION # 205
You are incrementally loading data into fact tables in an Azure Synapse Analytics dedicated SQL pool.
Each batch of incoming data is staged before being loaded into the fact tables. | You need to ensure that the incoming data is staged as quickly as possible. | How should you configure the staging tables? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation:

Round-robin distribution is recommended for staging tables because it distributes data evenly across all the distributions without requiring a hash column. This can improve the speed of data loading and avoid data skew. Heap tables are recommended for staging tables because they do not have any indexes or partitions that can slow down the data loading process. Heap tables are also easier to truncate and reload than clustered index or columnstore index tables.


NEW QUESTION # 206
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 are designing an Azure Stream Analytics solution that will analyze Twitter dat a.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a hopping window that uses a hop size of 5 seconds and a window size 10 seconds.
Does this meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:
Instead use a tumbling window. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics


NEW QUESTION # 207
......

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